Better than Expected or as Bad as You Thought? The Neurocognitive Development of Probabilistic Feedback Processing

Learning from feedback lies at the foundation of adaptive behavior. Two prior neuroimaging studies have suggested that there are qualitative differences in how children and adults use feedback by demonstrating that dorsolateral prefrontal cortex (DLPFC) and parietal cortex were more active after negative feedback for adults, but after positive feedback for children. In the current study we used functional magnetic resonance imaging (fMRI) to test whether this difference is related to valence or informative value of the feedback by examining neural responses to negative and positive feedback while applying probabilistic rules. In total, 67 healthy volunteers between ages 8 and 22 participated in the study (8–11 years, n = 18; 13–16 years, n = 27; 18–22 years, n = 22). Behavioral comparisons showed that all participants were able to learn probabilistic rules equally well. DLPFC and dorsal anterior cingulate cortex were more active in younger children following positive feedback and in adults following negative feedback, but only when exploring alternative rules, not when applying the most advantageous rules. These findings suggest that developmental differences in neural responses to feedback are not related to valence per se, but that there is an age-related change in processing learning signals with different informative value

Neural mechanisms underlying the induction and relief of perceptual curiosity

Curiosity is one of the most basic biological drives in both animals and humans, and has been identified as a key motive for learning and discovery. Despite the importance of curiosity and related behaviors, the topic has been largely neglected in human neuroscience; hence little is known about the neurobiological mechanisms underlying curiosity. We used functional magnetic resonance imaging (fMRI) to investigate what happens in our brain during the induction and subsequent relief of perceptual curiosity. Our core findings were that (1) the induction of perceptual curiosity, through the presentation of ambiguous visual input, activated the anterior insula and anterior cingulate cortex (ACC), brain regions sensitive to conflict and arousal; (2) the relief of perceptual curiosity, through visual disambiguation, activated regions of the striatum that have been related to reward processing; and (3) the relief of perceptual curiosity was associated with hippocampal activation and enhanced incidental memory. These findings provide the first demonstration of the neural basis of human perceptual curiosity. Our results provide neurobiological support for a classic psychological theory of curiosity, which holds that curiosity is an aversive condition of increased arousal whose termination is rewarding and facilitates memory

Whole brain resting-state analysis reveals decreased functional connectivity in major depression

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within six months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxelwise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: 1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, 2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and 3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or grey matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder

Clusterwise Independent Component Analysis (C-ICA):An R package for clustering subjects based on ICA patterns underlying three-way (brain) data

In many areas of science, like neuroscience, genomics and text mining, several important and challenging research questions imply the study of (subject) heterogeneity present in three-way data. In clinical neuroscience, for example, disclosing differences or heterogeneity between subjects in resting state networks (RSNs) underlying multi-subject fMRI data (i.e., time by voxel by subject three-way data) may advance the subtyping of psychiatric and mental diseases. Recently, the Clusterwise Independent Component Analysis (C-ICA) method was proposed that enables the disclosure of heterogeneity between subjects in RSNs that is present in multi-subject rs-fMRI data [1]. Up to now, however, no publicly available software exists that allows to fit C-ICA to empirical data at hand. The goal of this paper, therefore, is to present the CICA R package, which contains the necessary functions to estimate the C-ICA parameters and to interpret and visualize the analysis output. Further, the package also includes functions to select suitable initial values for the C-ICA model parameters and to determine the optimal number of clusters and components for a given empirical data set (i.e., model selection). The use of the main functions of the package is discussed and demonstrated with simulated data. Herewith, the necessary analytical choices that have to be made by the user (e.g., starting values) are explained and showed step by step. The rich functionality of the package is further illustrated by applying C-ICA to empirical rs-fMRI data from a group of Alzheimer patients and elderly control subjects and to multi-country stock market data. Finally, extensions regarding the C-ICA algorithm and procedures for model selection that could be implemented in future releases of the package are discussed

When I relive a positive me: Vivid autobiographical memories facilitate autonoetic brain activation and enhance mood

Autobiographical memory is vital for our well-being and therefore used in therapeutic interventions. However, not much is known about the (neural) processes by which reliving memories can have beneficial effects. This study investigates what brain activation patterns and memory characteristics facilitate the effectiveness of reliving positive autobiographical memories for mood and sense of self. Particularly, the role of vividness and autonoetic consciousness is studied. Participants (N= 47) with a wide range of trait self-esteem relived neutral and positive memories while their bold responses, experienced vividness of the memory, mood, and state self-esteem were recorded. More vivid memories related to better mood and activation in amygdala, hippocampus and insula,indicative of increased awareness of oneself (i.e., prereflective aspect of autonoetic con-sciousness). Lower vividness was associated with increased activation in the occipital lobe, PCC, and precuneus, indicative of a more distant mode of reliving. While individuals with lower trait self-esteem increased in state self-esteem, they showed less deacti-vation of the lateral occipital cortex during positive memories. In sum, the vividness of the memory seemingly distinguished a more immersed and more distant manner of memory reliving. In particular, when reliving positive memories higher vividness facilitated increased prereflective autonoetic consciousness, which likely is instrumental in boosting mood

Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity

BACKGROUND: Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data. METHODOLOGY/PRINCIPAL FINDINGS: 18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions. CONCLUSIONS/SIGNIFICANCE: We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease

Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease

Background: Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk. Methods: We acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E ϵ4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas o

Catecholaminergic neuromodulation shapes intrinsic MRI functional connectivity in the human brain

The brain commonly exhibits spontaneous (i.e., in the absence of a task) fluctuations in neural activity that are correlated across brain regions. It has been established that the spatial structure, or topography, of these intrinsic correlations is in part determined by the fixed anatomical connectivity between regions. However, it remains unclear which factors dynamically sculpt this topography as a function of brain state. Potential candidate factors are subcortical catecholaminergic neuromodulatory systems, such as the locus ceruleus-norepinephrine system, which send diffuse projections to most parts of the forebrain. Here, we systematically characterized the effects of endogenous central neuromodulation on correlated fluctuations during rest in the human brain. Using a double-blind placebo-controlled crossover design, we pharmacologically increased synaptic catecholamine levels by administering atomoxetine, an NE transporter blocker, and examined the effects on the strength and spatial structure of resting-state MRI functional connectivity. First, atomoxetine reduced the strength of inter-regional correlations across three levels of spatial organization, indicating that catecholamines reduce the strength of functional interactions during rest. Second, this modulatory effect on intrinsic correlations exhibited a substantial degree of spatial specificity: the decrease in functional connectivity showed an anterior-posterior gradient in the cortex, depended on the strength of baseline functional connectivity, and was strongest for connections between regions belonging to distinct resting-state networks. Thus, catecholamines reduce intrinsic correlations in a spatially heterogeneous fashion. We conclude that neuromodulation is an important factor shaping the topography of intrinsic functional connectivity

Linkage disequilibrium analysis to enable more efficient gene and QTL mapping in apple

BACKGROUND/OBJECTIVE: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI