Single-value brain activity scores reflect both severity and risk across the Alzheimer's continuum.

Single-value scores reflecting the deviation from (FADE score) or similarity with (SAME score) prototypical novelty-related and memory-related functional MRI activation patterns in young adults have been proposed as imaging biomarkers of healthy neurocognitive ageing. Here, we tested the utility of these scores as potential diagnostic and prognostic markers in Alzheimer's disease (AD) and risk states like mild cognitive impairment (MCI) or subjective cognitive decline (SCD). To this end, we analysed subsequent memory functional MRI data from individuals with SCD, MCI and AD dementia as well as healthy controls and first-degree relatives of AD dementia patients (AD-rel) who participated in the multi-centre DELCODE study (n = 468).

Working memory signals in early visual cortex are present in weak and strong imagers.

It has been suggested that visual images are memorized across brief periods of time by vividly imagining them as if they were still there. In line with this, the contents of both working memory and visual imagery are known to be encoded already in early visual cortex. If these signals in early visual areas were indeed to reflect a combined imagery and memory code, one would predict them to be weaker for individuals with reduced visual imagery vividness.

Age-related differences in fMRI subsequent memory effects are directly linked to local grey matter volume differences.

Episodic memory performance declines with increasing age, and older adults typically show reduced activation of inferior temporo-parietal cortices in functional magnetic resonance imaging (fMRI) studies of episodic memory formation. Given the age-related cortical volume loss, it is conceivable that age-related reduction of memory-related fMRI activity may be partially attributable to reduced grey matter volume (GMV). We performed a voxel-wise multimodal neuroimaging analysis of fMRI correlates of successful memory encoding, using regional GMV as covariate.

Encoding of continuous perceptual choices in human early visual cortex.

Introduction: Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex.

Methods: In this study, we used motion stimuli that could vary anywhere between 0° and 360° to assess how the brain encodes choices for features that span the full sensory continuum.

Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression.

Background: Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages.

Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults.

Successful explicit memory encoding is associated with inferior temporal activations and medial parietal deactivations, which are attenuated in aging. Here we used dynamic causal modeling (DCM) of functional magnetic resonance imaging data to elucidate effective connectivity patterns between hippocampus, parahippocampal place area (PPA), and precuneus during encoding of novel visual scenes. In 117 young adults, DCM revealed pronounced activating input from the PPA to the hippocampus and inhibitory connectivity from the PPA to the precuneus during novelty processing, with both being enhanced during successful encoding.

Openness to Experience is associated with neural and performance measures of memory in older adults.

Age-related decline in episodic memory performance is a well-replicated finding across numerous studies. Recent studies focusing on aging and individual differences found that the Big Five personality trait Openness to Experience (hereafter: Openness) is associated with better episodic memory performance in older adults, but the associated neural mechanisms are largely unclear. Here, we investigated the relationship between Openness and memory network function in a sample of 352 participants (143 older adults, 50-80 years; 209 young adults, 18-35 years).

The relationship between resting-state amplitude fluctuations and memory-related deactivations of the default mode network in young and older adults.

The default mode network (DMN) typically exhibits deactivations during demanding tasks compared to periods of relative rest. In functional magnetic resonance imaging (fMRI) studies of episodic memory encoding, increased activity in DMN regions even predicts later forgetting in young healthy adults. This association is attenuated in older adults and, in some instances, increased DMN activity even predicts remembering rather than forgetting.

Single-value scores of memory-related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging.

Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two single-value scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, we investigate the brain-behavior associations of these scores with age-related neurocognitive changes in 153 healthy middle-aged and older adults.

A tightly controlled fMRI dataset for receptive field mapping in human visual cortex.

Four right-handed, healthy subjects participated in a visual stimulation experiment. Subjects were viewing a dartboard-shaped flickering checkerboard stimulus, divided into 4 rings and 12 segments, defining 48 sectors in the visual field. Local contrast in each sector was continuously varying across four levels and updated every 3 s.

Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance.

Human cognitive abilities decline with increasing chronological age, with decreased explicit memory performance being most strongly affected. However, some older adults show "successful aging," that is, relatively preserved cognitive ability in old age. One explanation for this could be higher brain-structural integrity in these individuals.

Psychophysics and computational modeling of feature-continuous motion perception.

Sensory decision-making is frequently studied using categorical tasks, even though the feature space of most stimuli is continuous. Recently, it has become more common to measure feature perception in a gradual fashion, say when studying motion perception across the full space of directions. However, continuous reports can be contaminated by perceptual or motor biases.

Learning by Insight-Like Sudden Comprehension as a Potential Strategy to Improve Memory Encoding in Older Adults.

Several cognitive functions show a decline with advanced age, most prominently episodic memory. Problem-solving by insight represents a special associative form of problem-solving that has previously been shown to facilitate long-term memory formation. Recent neuroimaging evidence suggests that the encoding network involved in insight-based memory formation is largely hippocampus-independent.

Suppress Me if You Can: Neurofeedback of the Readiness Potential.

Voluntary movements are usually preceded by a slow, negative-going brain signal over motor areas, the so-called readiness potential (RP). To date, the exact nature and causal role of the RP in movement preparation have remained heavily debated. Although the RP is influenced by several motorical and cognitive factors, it has remained unclear whether people can learn to exert mental control over their RP, for example, by deliberately suppressing it.

Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults.

Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited by insufficient memory performance in older and particularly memory-impaired individuals.

Distributional Transformation Improves Decoding Accuracy When Predicting Chronological Age From Structural MRI.

When predicting a certain subject-level variable (e.g., age in years) from measured biological data (e.

Neurocan genome-wide psychiatric risk variant affects explicit memory performance and hippocampal function in healthy humans.

Alterations of the brain extracellular matrix (ECM) can perturb the structure and function of brain networks like the hippocampus, a key region in human memory that is commonly affected in psychiatric disorders. Here, we investigated the potential effects of a genome-wide psychiatric risk variant in the NCAN gene encoding the ECM proteoglycan neurocan (rs1064395) on memory performance, hippocampal function and cortical morphology in young, healthy volunteers. We assessed verbal memory performance in two cohorts (N = 572, 302) and found reduced recall performance in risk allele (A) carriers across both cohorts.

Inverse transformed encoding models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding.

Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF).

MACS - a new SPM toolbox for model assessment, comparison and selection.

Background: In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for statistical model inference.

New Method: We introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data.

How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging.

In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: "How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection", NeuroImage, vol.

Behavioral and Neural Manifestations of Reward Memory in Carriers of Low-Expressing versus High-Expressing Genetic Variants of the Dopamine D2 Receptor.

Dopamine is critically important in the neural manifestation of motivated behavior, and alterations in the human dopaminergic system have been implicated in the etiology of motivation-related psychiatric disorders, most prominently addiction. Patients with chronic addiction exhibit reduced dopamine D2 receptor (DRD2) availability in the striatum, and the TaqIA (rs1800497) and C957T (rs6277) genetic polymorphisms have previously been linked to individual differences in striatal dopamine metabolism and clinical risk for alcohol and nicotine dependence. Here, we investigated the hypothesis that the variants of these polymorphisms would show increased reward-related memory formation, which has previously been shown to jointly engage the mesolimbic dopaminergic system and the hippocampus, as a potential intermediate phenotype for addiction memory.

A negative relationship between ventral striatal loss anticipation response and impulsivity in borderline personality disorder.

Patients with borderline personality disorder (BPD) frequently exhibit impulsive behavior, and self-reported impulsivity is typically higher in BPD patients when compared to healthy controls. Previous functional neuroimaging studies have suggested a link between impulsivity, the ventral striatal response to reward anticipation, and prediction errors. Here we investigated the striatal neural response to monetary gain and loss anticipation and their relationship with impulsivity in 21 female BPD patients and 23 age-matched female healthy controls using functional magnetic resonance imaging (fMRI).

How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection.

Voxel-wise general linear models (GLMs) are a standard approach for analyzing functional magnetic resonance imaging (fMRI) data. An advantage of GLMs is that they are flexible and can be adapted to the requirements of many different data sets. However, the specification of first-level GLMs leaves the researcher with many degrees of freedom which is problematic given recent efforts to ensure robust and reproducible fMRI data analysis.

Inhibition of Information Flow to the Default Mode Network During Self-Reference Versus Reference to Others.

The default mode network (DMN), a network centered around the cortical midline, shows deactivation during most cognitive tasks and pronounced resting-state connectivity, but is actively engaged in self-reference and social cognition. It is, however, yet unclear how information reaches the DMN during social cognitive processing. Here, we addressed this question using dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data acquired during self-reference (SR) and reference to others (OR).