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Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.

Noninvasive therapies such as cognitive intervention could aid the prevention and treatment of Alzheimer disease (AD). In this article, Buschert et al . provide an overview of the current knowledge relating to the use of cognitive intervention in patients with AD, and discuss recent findings that indicate that this treatment provides substantial benefits for patients with cognitive deficits. The authors also review recent studies that have used neuroimaging techniques to identify biological changes within the brain that are associated with cognitive intervention. Alzheimer disease (AD) is one of the most prevalent chronic medical conditions affecting the elderly population. The effectiveness of approved antidementia drugs, however, is limited—licensed AD medications provide only moderate relief of clinical symptoms. Cognitive intervention is a noninvasive therapy that could aid prevention and treatment of AD. Data suggest that specifically designed cognitive interventions could impart therapeutic benefits to patients with AD that are associated with substantial biological changes within the brain. Moreover, evidence indicates that a combination of pharmacological and non-pharmacological interventions could provide greater relief of clinical symptoms than either intervention given alone. Functional and structural MRI studies have increased our understanding of the underlying neurobiological mechanisms of aging and neurodegeneration, but the use of neuroimaging to investigate the effect of cognitive intervention on the brain remains largely unexplored. This Review provides an overview of the use of cognitive intervention in the healthy elderly population and patients with AD, and summarizes emerging findings that provide evidence for the effectiveness of this approach. Finally, we present recommendations for future research on the use of cognitive interventions in AD and discuss potential effects of this therapy on disease modification. Key Points No disease-modifying drugs are available for the treatment of Alzheimer disease (AD) and the effectiveness of approved antidementia drugs is still not satisfactory Non-pharmacological interventions could aid the prevention and treatment of AD, and combining pharmacological and non-pharmacological interventions might substantially alleviate the clinical symptoms associated with the disease Neuroimaging studies could further our understanding of the neurobiological mechanisms underlying the effects of cognitive intervention on the brain Health-care professionals must base recommendations concerning the use of cognitive intervention in mild cognitive impairment and AD on robust experimental evidence No standardized intervention programs are currently available for the treatment of the diverse cognitive and functional impairments associated with the different stages of AD

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.

Moment-to-moment reaction time variability on tasks of attention, often quantified by intra-individual response variability (IRV), provides a good indication of the degree to which an individual is vulnerable to lapses in sustained attention. Increased IRV is a hallmark of several disorders of attention, including Attention-Deficit/Hyperactivity Disorder (ADHD). Here, task-based fMRI was used to provide the first examination of how average brain activation and functional connectivity patterns in adolescents are related to individual differences in sustained attention as measured by IRV. We computed IRV in a large sample of adolescents (n = 758) across 'Go' trials of a Stop Signal Task (SST). A data-driven, multi-step analysis approach was used to identify networks associated with low IRV (i.e., good sustained attention) and high IRV (i.e., poorer sustained attention). Low IRV was associated with greater functional segregation (i.e., stronger negative connectivity) amongst an array of brain networks, particularly between cerebellum and motor, cerebellum and prefrontal, and occipital and motor networks. In contrast, high IRV was associated with stronger positive connectivity within the motor network bilaterally and between motor and parietal, prefrontal, and limbic networks. Consistent with these observations, a separate sample of adolescents exhibiting elevated ADHD symptoms had increased fMRI activation and stronger positive connectivity within the same motor network denoting poorer sustained attention, compared to a matched asymptomatic control sample. With respect to the functional connectivity signature of low IRV, there were no statistically significant differences in networks denoting good sustained attention between the ADHD symptom group and asymptomatic control group. We propose that sustained attentional processes are facilitated by an array of neural networks working together, and provide an empirical account of how the functional role of the cerebellum extends to cognition in adolescents. This work highlights the involvement of motor cortex in the integrity of sustained attention, and suggests that atypically strong connectivity within motor networks characterizes poor attentional capacity in both typically developing and ADHD symptomatic adolescents.

Resting state functional magnetic resonance imaging (fMRI) reveals a distinct network of correlated brain function representing a default mode state of the human brain. The underlying structural basis of this functional connectivity pattern is still widely unexplored. We combined fractional anisotropy measures of fiber tract integrity derived from diffusion tensor imaging (DTI) and resting state fMRI data obtained at 3 Tesla from 20 healthy elderly subjects (56 to 83 years of age) to determine white matter microstructure underlying default mode connectivity. We hypothesized that the functional connectivity between the posterior cingulate and hippocampus from resting state fMRI data would be associated with the white matter microstructure in the cingulate bundle and fiber tracts connecting posterior cingulate gyrus with lateral temporal lobes, medial temporal lobes, and precuneus. This was demonstrated at the p<0.001 level using a voxel-based multivariate analysis of covariance (MANCOVA) approach. In addition, we used a data-driven technique of joint independent component analysis (ICA) that uncovers spatial pattern that are linked across modalities. It revealed a pattern of white matter tracts including cingulate bundle and associated fiber tracts resembling the findings from the hypothesis-driven analysis and was linked to the pattern of default mode network (DMN) connectivity in the resting state fMRI data. Our findings support the notion that the functional connectivity between the posterior cingulate and hippocampus and the functional connectivity across the entire DMN is based on distinct pattern of anatomical connectivity within the cerebral white matter.

Automated deformation-based analysis of MRI scans can be used to detect specific pattern of brain atrophy in Alzheimer's disease (AD), but it still lacks an established model to derive the individual risk of AD in at-risk subjects, such as patients with mild cognitive impairment (MCI). We applied principal component analysis to deformation maps derived from MRI scans of 32 AD patients, 18 elderly healthy controls and 24 MCI patients. Principal component scores were used to discriminate between AD patients and controls and between MCI converters and MCI non-converters. We found a significant regional pattern of atrophy (p<0.001) in medial temporal lobes, neocortical association areas, thalamus and basal ganglia and corresponding widening of cerebrospinal fluid (CSF) spaces (p<0.001) in AD patients compared to controls. Accuracy was 81% for CSF- and 83% for brain-based deformation maps to separate AD patients from controls. Nine out of 24 MCI patients converted to AD during clinical follow-up. Discrimination between MCI converters and non-converters reached 80% accuracy based on CSF maps and 73% accuracy based on brain maps. In a logistic regression model, principal component scores based on CSF maps predicted clinical outcome in MCI patients even after controlling for age, gender, MMSE score and time of follow-up. Our findings indicate that multivariate network analysis of deformation maps detects typical features of AD pathology and provides a powerful tool to predict conversion into AD in non-demented at risk patients.

Key Points Recent research progress into the molecular pathogenesis of Alzheimer's disease has been translated into several promising drug candidates that have the potential to produce disease-modifying effects. Biomarkers that reflect the central pathogenic processes in Alzheimer's disease have been developed and validated in numerous studies. These include cerebrospinal fluid (CSF) assays for tau and amyloid-β isoforms; magnetic resonance imaging (MRI) measurements of brain atrophy; and positron emission tomography (PET) techniques for brain metabolism and amyloid-β deposition. Academic institutions, the pharmaceutical industry and regulatory organizations all agree that biomarkers have an important role in the drug development process. Biomarkers have several potential uses in clinical trials. These include their use as diagnostic aids to enrich the patient sample with cases of Alzheimer's disease; as tools to identify and monitor the biochemical effect of the drug candidate; and as safety markers to detect potential side effects of the drug. Evidence obtained from biomarker studies showing that a drug candidate affects the central disease processes in Alzheimer's disease will, together with a beneficial effect on cognition, be essential for the drug to be labelled as disease-modifying. A catch-22-like situation exists in validating Alzheimer's disease biomarkers for use in drug development. Biomarker validation depends on effective drugs that target Alzheimer's disease pathogenesis, which are not currently available. At the same time, evidence from biomarker studies is needed for a new drug to be labelled as disease-modifying. There are numerous ongoing clinical trials investigating disease-modifying drug candidates, which include biomarkers as end points. These trials will provide information on whether biomarkers will be valuable tools as surrogate end points to predict the clinical outcome and as the basis for a disease-modifying claim of the drug. If disease-modifying drugs are approved for the treatment of Alzheimer's disease, biomarkers will facilitate the diagnosis of Alzheimer's disease very early on in the course of the disease before neurodegeneration is too severe and widespread. Advances in therapeutic strategies for Alzheimer's disease depend on the identification and qualification of biomarkers. Here, the authors review the current status of candidate biomarkers for Alzheimer's disease and provide the perspectives of different stakeholders on biomarker discovery and development. Advances in therapeutic strategies for Alzheimer's disease that lead to even small delays in onset and progression of the condition would significantly reduce the global burden of the disease. To effectively test compounds for Alzheimer's disease and bring therapy to individuals as early as possible there is an urgent need for collaboration between academic institutions, industry and regulatory organizations for the establishment of standards and networks for the identification and qualification of biological marker candidates. Biomarkers are needed to monitor drug safety, to identify individuals who are most likely to respond to specific treatments, to stratify presymptomatic patients and to quantify the benefits of treatments. Biomarkers that achieve these characteristics should enable objective business decisions in portfolio management and facilitate regulatory approval of new therapies.

Contemporary theories propose that dysregulation of emotional perception is involved in the aetiology of psychosis. 298 healthy adolescents were assessed at age 14- and 19-years using fMRI while performing a facial emotion task. Psychotic-like experiences (PLEs) were assessed with the CAPE-42 questionnaire at age 19. The high PLEs group at age 19 years exhibited an enhanced response in right insular cortex and decreased response in right prefrontal, right parahippocampal and left striatal regions; also, a gradient of decreasing response to emotional faces with age, from 14 to 19 years, in the right parahippocampal region and left insular cortical area. The right insula demonstrated an increasing response to emotional faces with increasing age in the low PLEs group, and a decreasing response over time in the high PLEs group. The change in parahippocampal/amygdala and insula responses during the perception of emotional faces in adolescents with high PLEs between the ages of 14 and 19 suggests a potential ‘aberrant’ neurodevelopmental trajectory for critical limbic areas. Our findings emphasize the role of the frontal and limbic areas in the aetiology of psychotic symptoms, in subjects without the illness phenotype and the confounds introduced by antipsychotic medication.

Adolescence is a vulnerable time for personality development. Especially neuroticism with its link to the development of psychopathology is of interest concerning influential factors. The present study exploratorily investigates neuroanatomical signatures for developmental trajectories of neuroticism based on a voxel-wise whole-brain structural equation modelling framework. In 1,814 healthy adolescents of the IMAGEN sample, the NEO-FFI was acquired at three measurement occasions across five years. Based on a partial measurement invariance second-order latent growth curve model we conducted whole-brain analyses on structural MRI data at age 14 years, predicting change in neuroticism over time. We observed that a reduced volume in the pituitary gland was associated with the slope of neuroticism over time. However, no relations with prefrontal areas emerged. Both findings are discussed against the background of possible genetic and social influences that may account for this result.

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the STRATIFY & ESTRA Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities. Evidence from large longitudinal neuroimaging cohorts, which include genetic and behavioral data, suggest a common neural basis for symptoms seen across multiple psychiatric disorders.