Healthy aging (HA) is associated with certain declines in cognitive functions, even in individuals that are free of any process of degenerative illness. Functional magnetic resonance imaging (fMRI) ...has been widely used in order to link this age-related cognitive decline with patterns of altered brain function. A consistent finding in the fMRI literature is that healthy old adults present higher activity levels in some brain regions during the performance of cognitive tasks. This finding is usually interpreted as a compensatory mechanism. More recent approaches have focused on the study of functional connectivity, mainly derived from resting state fMRI, and have concluded that the higher levels of activity coexist with disrupted connectivity. In this review, we aim to provide a state-of-the-art description of the usefulness and the interpretations of functional brain connectivity in the context of HA. We first give a background that includes some basic aspects and methodological issues regarding functional connectivity. We summarize the main findings and the cognitive models that have been derived from task-activity studies, and we then review the findings provided by resting-state functional connectivity in HA. Finally, we suggest some future directions in this field of research. A common finding of the studies included is that older subjects present reduced functional connectivity compared to young adults. This reduced connectivity affects the main brain networks and explains age-related cognitive alterations. Remarkably, the default mode network appears as a highly compromised system in HA. Overall, the scenario given by both activity and connectivity studies also suggests that the trajectory of changes during task may differ from those observed during resting-state. We propose that the use of complex modeling approaches studying effective connectivity may help to understand context-dependent functional reorganizations in the aging process.
The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have ...been few assessments of their differences, making it difficult to choose approaches, and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation, and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p < .05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high‐dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.
There is a growing realization that early life influences have lasting impact on brain function and structure. Recent research has demonstrated that genetic relationships in adults can be used to ...parcellate the cortex into regions of maximal shared genetic influence, and a major hypothesis is that genetically programmed neurodevelopmental events cause a lasting impact on the organization of the cerebral cortex observable decades later. Here we tested how developmental and lifespan changes in cortical thickness fit the underlying genetic organizational principles of cortical thickness in a longitudinal sample of 974 participants between 4.1 and 88.5 y of age with a total of 1,633 scans, including 773 scans from children below 12 y. Genetic clustering of cortical thickness was based on an independent dataset of 406 adult twins. Developmental and adult age-related changes in cortical thickness followed closely the genetic organization of the cerebral cortex, with change rates varying as a function of genetic similarity between regions. Cortical regions with overlapping genetic architecture showed correlated developmental and adult age change trajectories and vice versa for regions with low genetic overlap. Thus, effects of genes on regional variations in cortical thickness in middle age can be traced to regional differences in neurodevelopmental change rates and extrapolated to further adult aging-related cortical thinning. This finding suggests that genetic factors contribute to cortical changes through life and calls for a lifespan perspective in research aimed at identifying the genetic and environmental determinants of cortical development and aging.
Background and purpose
How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker‐confirmed early‐onset (EOAD) and late‐onset (LOAD) Alzheimer's disease ...(AD) was assessed.
Method
Eighty‐seven cerebrospinal fluid (CSF) biomarker‐confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non‐carriers (–EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non‐carriers (–LOAD). Grey matter (GM) volume differences were analyzed using voxel‐based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition.
Results
Significantly more mammillary body atrophy in +EOAD compared to –EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to –LOAD. Medial temporal GM volume loss was also found in +EOAD compared to –LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in –LOAD.
Conclusions
Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to –LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.
Objectives
Although previous studies have proposed a positive influence of resilience on the mental and physical health of health care workers, empirical evidence on its relationship with ...occupational health remains scarce. This study aimed to analyze the relationship between individual resilience and several occupational health indicators, as well as exploring the moderating role of organizational resilience and sociodemographic attributes on this relationship.
Methods
A cross-sectional design was used with a questionnaire applied to a sample of 325 workers from the Spanish health care sector.
Results
Individual resilience was significantly associated with the indicators of occupational health. A direct effect of individual resilience on job satisfaction was found. The influence of resilience on the perception of fatigue and suffering from an illness was reverse. Age moderated the impact of resilience on the perception of stress and medical leave. Besides, organizational resilience proved to be an important adjustment variable in job satisfaction and perception of stress.
Conclusions
The findings show the relevance to take both individual and organizational resilience into account when applying intervention programs to improve the occupational health of health care workers.
Abstract Cerebrospinal fluid (CSF) neurofilament light (NFL) is a marker of axonal degeneration. We tested whether CSF NFL levels predict hippocampal atrophy rate in cognitively healthy older adults ...independently of the established CSF Alzheimer’s disease (AD) biomarkers, β-amyloid 1-42 (Aβ42) and phosphorylated tau (P-tau). We included 144 participants in a 2-year longitudinal study with baseline CSF measures and two magnetic resonance images. 88 participants had full data available. A subgroup of 36 participants with very low AD risk was also studied. NFL predicted hippocampal atrophy rate independently of age, Aβ42 and P-tau. Including NFL, P-tau and age in the same model, higher NFL and lower P-tau predicted higher hippocampal atrophy (R2 =.20, NFL: β =-.34;p=.003, P-tau: β =.27;p=.009). The results were upheld in the participants with very low AD risk. NFL predicted neurodegeneration in older adults with very low AD probability. We suggest that factors previously shown to be important for brain degeneration in mild cognitive impairment may also impact changes in normal aging, demonstrating that NFL is likely to indicate AD-independent, age-expected neurodegeneration.
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and ...to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T‐T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2‐year follow‐up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross‐sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross‐sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross‐sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross‐sectional and longitudinal data.
We use machine learning tools in a multidisease approach to assess differential discrimination of Alzheimer's disease and frontotemporal dementia. Importantly, our approach includes a feature reduction strategy, making it computationally efficient and it also considers the study of associated brain patterns to gain explainability of the algorithm.
Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in ...their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein Aβ42, total‐tau protein T‐tau, neurofilament light chain NfL, neurogranin Ng, and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD.
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