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It is unclear whether cerebrospinal fluid (CSF) biomarkers of neurodegeneration predict brain atrophy in cognitively healthy older adults, whether these associations can be explained by phosphorylated tau181 (p-tau) and the 42 amino acid form of amyloid-β (Aβ42) biomarkers, and which neural substrates may drive these associations. We addressed these questions in 2 samples of cognitively healthy older adults who underwent longitudinal structural MRI up to 7 years and had baseline CSF levels of heart-type fatty-acid binding protein (FABP3)=, total-tau, neurogranin, and neurofilament light (NFL) (n = 189, scans = 721). The results showed that NFL, total-tau, and FABP3 predicted entorhinal thinning and hippocampal atrophy. Brain atrophy was not moderated by Aβ42 and the associations between NFL and FABP3 with brain atrophy were independent of p-tau. The spatial pattern of cortical atrophy associated with the biomarkers overlapped with neurogenetic profiles associated with expression in the axonal (total-tau, NFL) and dendritic (neurogranin) components. CSF biomarkers of neurodegeneration are useful for predicting specific features of brain atrophy in older adults, independently of amyloid and tau pathology biomarkers.

Brain changes occurring in aging can be indexed by biomarkers. We used cluster analysis to identify subgroups of cognitively unimpaired individuals (n = 99, 64–93 years) with different profiles of the cerebrospinal fluid biomarkers beta amyloid 1–42 (Aβ42), phosphorylated tau (P-tau), total tau, chitinase-3-like protein 1 (YKL-40), fatty acid binding protein 3 (FABP3), and neurofilament light (NFL). Hippocampal volume and memory were assessed across multiple follow-up examinations covering up to 6.8 years. Clustering revealed one group (39%) with more pathological concentrations of all biomarkers, which could further be divided into one group (20%) characterized by tauopathy and high FABP3 and one (19%) by brain β-amyloidosis, high NFL, and slightly higher YKL-40. The clustering approach clearly outperformed classification based on Aβ42 and P-tau alone in prediction of memory decline, with the individuals with most tauopathy and FABP3 showing more memory decline, but not more hippocampal volume change. The results demonstrate that older adults can be classified based on biomarkers beyond amyloid and tau, with improved prediction of memory decline.

Age-related differences in white matter (WM) integrity are substantial, but it is unknown whether between-subject variability in WM integrity influences the capacity for cognitive improvement. We investigated the effects of memory training related to active and passive control conditions in older adults and tested whether WM integrity at baseline was predictive of training benefits. We hypothesized that (1) memory improvement would be restricted to the training group, (2) widespread areas would show greater mean diffusivity (MD) and lower fractional anisotropy in older adults relative to young adults, and (3) within these areas, variability in WM microstructure in the older group would be predictive of training gains. The results showed that only the group receiving training improved their memory. Significant age differences in MD and fractional anisotropy were found in widespread areas. Within these areas, voxelwise analyses showed a negative relationship between MD and memory improvement in 3 clusters, indicating that WM integrity could serve as a marker for the ability to adapt in response to cognitive challenges in aging.

In this perspective paper, we examine possible premises of plasticity in the neural substrates underlying cognitive change. We take the special role of the medial temporal lobe as an anchoring point, but also investigate characteristics throughout the cortex. Specifically, we examine the dimensions of evolutionary expansion, heritability, variability of morphometric change, and inter-individual variance in myelination with respect to the plastic potential of different brain regions. We argue that areas showing less evolutionary expansion, lower heritability, greater variability of cortical thickness change through the lifespan, and greater inter-individual differences in intracortical myelin content have a great extent of plasticity. While different regions of the brain show these features to varying extent, analyses converge on the medial temporal lobe including the hippocampi as the target of all these premises. We discuss implications for effects of training on brain structures, and conditions under which plasticity may be evoked.

The purpose of the present study was to detail the childhood developmental course of different white matter (WM) characteristics. In a longitudinal diffusion tensor imaging (DTI) study of 159 healthy children between 4 and 11years scanned twice, we used tract-based spatial statistics as well as delineation of 15 major WM tracts to characterize the regional pattern of change in fractional anisotropy (FA), mean (MD), radial (RD) and axial diffusivity (AD). We tested whether there were decelerations of change with increasing age globally and tract-wise, and also illustrated change along medial-to-lateral, posterior-to-anterior and inferior-to-superior gradients. We found a significant linear increase in global FA, and decrease in MD and RD over time. For mean AD, a weak decrease was observed. The developmental changes in specific WM tracts showed regional differences. Eight WM tracts showed non-linear development patterns for one or several DTI metrics, with a deceleration in change with age. Sex did not affect change in any DTI metric. Overall, greater rate of change was found in the left hemisphere. Spatially, there was a posterior-to-anterior gradient of change with greater change in frontal regions for all metrics. The current study provides a comprehensive characterization of the regional patters of change in WM microstructure across pre-adolescence childhood.

Alzheimer's disease (AD) has traditionally been regarded as a disease of the gray matter (GM). However, the advent of diffusion tensor imaging (DTI) has contributed to new knowledge about how changes in white matter (WM) microstructure in vivo may be directly related to the pathophysiology of AD. It is now evident that WM is heavily affected in AD, even at early stages. Still, our knowledge about WM degeneration in AD is poor compared to what we know about GM atrophy. For instance, it has not been clear if WM can be directly affected in AD independently of GM degeneration, or whether WM changes mainly represent secondary effects of GM atrophy, e.g. through Wallerian degeneration. In this paper, we review recent studies using DTI to study WM alterations in AD. These studies suggest that microstructural WM affection at pre-AD stages cannot completely be accounted for by concomitant GM atrophy. Further, recent research has demonstrated relationships between increased cerebrospinal fluid levels of Tau proteins and changes in WM microstructure indexed by DTI, which could indicate that WM degeneration in pre-AD stages is related to ongoing axonal damage. We conclude that DTI is a promising biomarker for AD, with the potential also to identify subgroups of patients with especially high degree of WM affection, thereby contributing to more differentiated pre-AD diagnoses. However, more research and validation studies are needed before it is realistic to use this information in clinical practice with individual patients.

Background Employing apps in assessment of symptoms during cancer treatment facilitates detection of adverse events and may improve patient outcomes. Our interactive app Interaktor supports patients’ symptom management by regular symptom reporting, alerts for rapid access to staff in case of severe symptoms and continual access to relevant self-care advice. The aim of this study was to describe usage and explore perceptions of using Interaktor during cancer treatment. Methods The study includes the participants from the intervention arms of two separate randomized controlled trials (RCT) on patients with breast cancer (n = 74) during neoadjuvant chemotherapy and locally advanced prostate cancer (n = 75) during radiotherapy. It comprises usage metrics analysed by descriptive statistics and interviews analyzed by conventional content analysis. Results Adherence to daily reporting during treatment was in median above 80 %. Most patients viewed the information pages with self-care advice numerous times. The app was perceived user-friendly, faciliating interaction with health care professionals and supporting self care. Symptom reporting was a quick and comfortable way to access help. Using the app generated feelings of being monitored, involved and cared for. Further it supported attentiveness to and reflection of own well- being but could also serve as a reminder of illness. Some patients described that vigor, comorbidity, and cognitive side effects from the treatment influenced the motivation for and ability to symptom report. The information pages with self care advice were useful and appreciated and gave an idea of what was to be expected during treatment. Patients requested added and more comprehensive information on psychological symptoms and dietary advice. Conclusions The Interaktor app is a userfriendly and convenient alternative for patients contact and involvement with health care that aided symptom management, self-care and further enhanced patients participation in their care. Clinical trial identification NCT02479607 and NCT02477137. Legal entity responsible for the study Karolinska Institutet. Funding Swedish Cancer Foundation, Swedish Research Council, Swedish Research Council for Health, Working Life and Welfare and Karolinska Institutet. Disclosure All authors have declared no conflicts of interest.

Advances in neuroimaging techniques have recently provided glimpse into the neurobiology of complex traits of human personality. Whereas some intriguing findings have connected aspects of personality to variations in brain morphology, the relations are complex and our current understanding is incomplete. Therefore, we aimed to provide a comprehensive investigation of brain-personality relations using a multimodal neuroimaging approach in a large sample comprising 265 healthy individuals. The NEO Personality Inventory was used to provide measures of core aspects of human personality, and imaging phenotypes included measures of total and regional brain volumes, regional cortical thickness and arealization, and diffusion tensor imaging indices of white matter (WM) microstructure. Neuroticism was the trait most clearly linked to brain structure. Higher neuroticism including facets reflecting anxiety, depression and vulnerability to stress was associated with smaller total brain volume, widespread decrease in WM microstructure, and smaller frontotemporal surface area. Higher scores on extraversion were associated with thinner inferior frontal gyrus, and conscientiousness was negatively associated with arealization of the temporoparietal junction. No reliable associations between brain structure and agreeableness and openness, respectively, were found. The results provide novel evidence of the associations between brain structure and variations in human personality, and corroborate previous findings of a consistent neuroanatomical basis of negative emotionality.

Although some older adults experiencing memory problems have been shown to benefit from cognitive training, evidence regarding who will improve from this type of intervention is lacking. Automated hippocampal volumetry might be used to foresee treatment outcomes. We hypothesized that larger hippocampal volumes are associated with greater memory performance changes following training, and that effects are selectively related to specific hippocampal subfields. 19 memory clinic outpatients with subjective memory impairment (mean age=60.9 years) underwent MRI-scanning and then followed an eight week training scheme aimed at improving verbal memory. We assessed verbal memory before and after training, and tested whether pretraining hippocampal volumes were related to memory improvements. To delineate regional specificity, we employed a new technique enabling automated volumetry of seven hippocampal subfields - including the cornu ammonis (CA) sectors and the dentate gyrus (DG). The results showed that larger hippocampal volumes before training were related to greater verbal recall improvements. Subfield volumetry revealed specific correlations between memory improvement and pretraining volumes of the left CA2/3 and CA4/DG. Depressive symptoms further gave a unique contribution in predicting gain of the intervention, independent of hippocampal volume. The results indicated that subjects with a stronger depressive symptom load benefited more from the training. A prediction model including baseline CA2/3-volume and depressive symptoms explained 42% of the variation in recall improvement. Our results are the first to suggest that hippocampal subfield volumetry is related to intervention outcomes in older adults experiencing memory problems. Also, previous studies have tended to exclude patients with concomitant depressive symptoms and memory complaints. The present results, however, strengthen the rationale and potential for cognitive intervention in these patients.

When people make mistakes in speeded cognitive tasks, their response time on the next trial will typically be slower. This is referred to as post-error slowing (PES), and is important for optimization of performance, but its exact function remains to be decided. However, although PES is relatively stable over time, we have almost no knowledge about how PES is affected by structural brain characteristics. The aim of this study was to test to what extent white matter (WM) macro- and microstructure can account for individual differences in PES. PES was calculated for 255 healthy participants who performed a modified version of the Eriksen flanker task and underwent structural magnetic resonance imaging and diffusion tensor imaging (DTI). PES was positively related to WM volume in the caudal and rostral middle and superior frontal, medial orbitofrontal gyri and pars orbitalis. DTI analyses with tract-based spatial statistics (TBSS) showed that mean diffusivity in the superior longitudinal fasciculus, inferior fronto-occipital fasciculus and anterior thalamic radiation, as well as axial diffusivity in the corpus callosum, was negatively related to PES. Path analysis demonstrated that WM micro- and macrostructure were complementary in accounting for PES. It is concluded that individual differences in WM characteristics can partly explain why some people are better at adjusting their behavior in response to poor performance than others.

Diffusion tensor imaging (DTI) and CSF biomarkers are useful diagnostic tools to differentiate patients with mild cognitive impairment (MCI) from normal controls, and may help predict conversion to dementia. Total Tau protein (T-tau) and DTI parameters are both markers for axonal damage, thus it is of interest to determine if DTI parameters are associated with elevated CSF T-tau levels in patients with cognitive impairment. For this purpose, patients with subjective cognitive impairment (SCI) and MCI were recruited from a university based memory clinic. Regions of interest were used to determine fractional anisotropy (FA), radial diffusivity (DR) and axial diffusivity (DA) in known white matter tracts in patients with MCI (n=39) and SCI (n=8) and 26 cognitively healthy controls. Significant lower FA and higher DR values were observed in patients with pathological vs. patients with normal CSF T-tau levels and vs. controls in left posterior cingulum fibers. T-tau values were negatively correlated with FA and positively correlated with DR values in the posterior cingulum fibers. Cingulum fiber diffusivity was related to T-tau pathology in SCI/MCI patients and altered DR may suggest that loss of myelin contributes to early white matter changes in patients at risk of developing Alzheimer's disease (AD).

Cortical thickness decreases from childhood throughout life, as estimated by magnetic resonance imaging (MRI). This monotone trajectory does not reflect the fundamentally different neurobiological processes underlying morphometric changes in development versus aging. We hypothesized that intracortical gray matter (GM) and subjacent white matter (WM) T1-weighted signal intensity would distinguish developmental and age-related changes in the cortex better than thickness. Intracortical GM and subjacent WM signal intensity and cortical thickness was measured across the brain surface in a healthy life span sample (n=429, 8-85 years). We also computed the relaxation rate of T2* (R2*) from multiecho sequences and mapped intracortical GM and subjacent WM values to the surface to delineate age-related variability in R2* and to adjust the T1 signal intensity for possible confounds of accumulated iron. While monotone age-related reductions in thickness were found, both intracortical GM and subcortical WM signal intensity showed inverted U patterns with peaks from eight to approximately 30 years of age. The spatial pattern of intracortical neurodevelopment followed a posterior-anterior gradient, with earliest maturation of occipital visual cortices and most protracted in superior frontal regions. From 50s and 60s, substantial signal reductions were observed in several regions, including the insula, cingulate, and inferior temporal gyrus. R2* showed similar patterns but peaked much later than the T1-weighted signal intensity measures. The results are presented as animations yielding detailed depictions of the dynamic regional variability in cortical neurodevelopment and aging and demonstrate that cortical thickness and T1-weighted signal intensity are sensitive to different cortical maturational and aging-related processes.

This study (n = 161) related morphometric MR imaging, FDG-PET and APOE genotype to memory scores in normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Stepwise regression analyses focused on morphometric and metabolic characteristics of the episodic memory network: hippocampus, entorhinal, parahippocampal, retrosplenial, posterior cingulate, precuneus, inferior parietal, and lateral orbitofrontal cortices. In NC, hippocampal metabolism predicted learning; entorhinal metabolism predicted recognition; and hippocampal metabolism predicted recall. In MCI, thickness of the entorhinal and precuneus cortices predicted learning, while parahippocampal metabolism predicted recognition. In AD, posterior cingulate cortical thickness predicted learning, while APOE genotype predicted recognition. In the total sample, hippocampal volume and metabolism, cortical thickness of the precuneus, and inferior parietal metabolism predicted learning; hippocampal volume and metabolism, parahippocampal thickness and APOE genotype predicted recognition. Imaging methods appear complementary and differentially sensitive to memory in health and disease. Medial temporal and parietal metabolism and morphometry best explained memory variance. Medial temporal characteristics were related to learning, recall and recognition, while parietal structures only predicted learning.

Many brain structures show a complex, non-linear pattern of maturation and age-related change. Often, quadratic models (β0+ β1age + β2age2+ ε) are used to describe such relationships. Here, we demonstrate that the fitting of quadratic models is substantially affected by seemingly irrelevant factors, such as the age-range sampled. Hippocampal volume was measured in 434 healthy participants between 8 and 85 years of age, and quadratic models were fit to subsets of the sample with different age-ranges. It was found that as the bottom of the age-range increased, the age at which volumes appeared to peak was moved upwards and the estimated decline in the last part of the age-span became larger. Thus, whether children were included or not affected the estimated decline between 60 and 85 years. We conclude that caution should be exerted in inferring age-trajectories from global fit models, e.g. the quadratic model. A nonparametric local smoothing technique (the smoothing spline) was found to be more robust to the effects of different starting ages. The results were replicated in an independent sample of 309 participants. © 2010 Elsevier Inc. All rights reserved.

Instability in performance is a prominent feature of aging. In this study, 133 participants evenly distributed across the adult lifespan underwent a three-stimulus event-related potentials (ERPs) paradigm, and instability in P3a/P3b latency and reaction time (RT) were measured. P3a is related to alertness or reorientation of attention and it was elicited by distractor stimuli. P3b is related to controlled attentional processes and it was triggered by target stimuli. Maximum-likelihood estimation (MLE) was used to quantify the level of variability in latency across the single sweep ERPs. The results revealed increased variability in RT but not in P3a/P3b latency with age. Variability in P3a/P3b latency was related to executive functions, and variability in RT to speed. Generally, the relationships tended to increase with age. It can be concluded that increased variability in RT with age is caused by instability in processes related to response execution and possibly response selection, but not stimulus classification. Further, the results indicate that intraindividual variability is not a uniform concept, but has unique explanatory value at the neurophysiological and behavioral level.

MRI-based estimates of cerebral morphometric properties, e.g. cortical thickness, are pivotal to studies of normal and pathological brain changes. These measures are based on automated or manual segmentation procedures, which utilize the tissue contrast between gray and white matter on T(1)-weighted MR images. Tissue contrast is unlikely to remain a constant property across groups of different age and health. An important question is therefore how the sensitivity of cortical thickness estimates is influenced by variability in WM/GM contrast. The effect of adjusting for variability in WM/GM contrast on age sensitivity of cortical thickness was tested in 1189 healthy subjects from six different samples, enabling evaluation of consistency of effects within and between sites and scanners. Further, the influence of Alzheimer's disease (AD) diagnosis on cortical thickness with and without correction for contrast was tested in an additional sample of 96 patients. In healthy controls, regional increases in the sensitivity of the cortical thickness measure to age were found after correcting for contrast. Across samples, the strongest effects were observed in frontal, lateral temporal and parietal areas. Controlling for contrast variability also increased the cortical thickness estimates' sensitivity to AD, thus replicating the finding in an independent clinical sample. The results showed increased sensitivity of cortical estimates to AD in areas earlier reported to be compromised in AD, including medial temporal, inferior and superior parietal regions. In sum, the findings indicate that adjusting for contrast can increase the sensitivity of MR morphometry to variables of interest.

This study compared sensitivity of FDG-PET, MR morphometry, and diffusion tensor imaging (DTI) derived fractional anisotropy (FA) measures to diagnosis and memory function in mild cognitive impairment (MCI). Patients ( n = 44) and normal controls (NC, n = 22) underwent FDG-PET and MRI scanning yielding measures of metabolism, morphometry and FA in nine temporal and parietal areas affected by Alzheimer's disease and involved in the episodic memory network. Patients also underwent memory testing (RAVLT). Logistic regression analysis yielded 100% diagnostic accuracy when all methods and ROIs were combined, but none of the variables then served as unique predictors. Within separate ROIs, diagnostic accuracy for the methods combined ranged from 65.6% (parahippocampal gyrus) to 73.4 (inferior parietal cortex). Morphometry predicted diagnostic group for most ROIs. PET and FA did not uniquely predict group, but a trend was seen for the precuneus metabolism. For the MCI group, stepwise regression analyses predicting memory scores were performed with the same methods and ROIs. Hippocampal volume and FA of the retrosplenial WM predicted learning, and hippocampal metabolism and parahippocampal cortical thickness predicted 5 minute recall. No variable predicted 30 minute recall independently of learning. In conclusion, higher diagnostic accuracy was achieved when multiple methods and ROIs were combined, but morphometry showed superior diagnostic sensitivity. Metabolism, morphometry and FA all uniquely explained memory performance, making a multi-modal approach superior. Memory variation in MCI is likely related to conversion risk, and the results indicate potential for improved predictive power by the use of multimodal imaging.