Intertemporal choice: Neuronal and psychological determinants of economic decisions.

This introductory article provides a short overview of empirical and theoretical articles presented in the special issue on psychological and neural models of intertemporal decision making, which is divided into 2 parts. The first part consists of contributions presenting different models of intertemporal choice. These contributions provide an overview of current conceptualizations; that is, providing several psychological and neural frameworks, investigating how memory processes are related to the anticipation of time, and showing how the perception of time underlies our decisions about the future. A final article deals with factors that influence choices on environmental policies where the consequences of decisions are delayed by decades or more. The second upcoming part is concerned with functional neuroimaging of intertemporal decision making. Two reviews of studies in neuroimaging and 2 empirical articles examine the questions of which brain regions (and associated functions) are involved in deciding on options with different temporal consequences. We hope this volume will be conducive in developing a better understanding of intertemporal decision making as part of complex sets of neural processes as well as psychological factors that include cognitive reasoning, emotional states, and the interconnected perception of time. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Functional neuroimaging of intertemporal choice models: A review.

People often forsake a larger reward later for a smaller reward sooner. The process of devaluing the larger, later prize is called temporal discounting or delay discounting, which lies at the core of intertemporal choice. Here, we describe the methodology and findings of research on the mechanisms of intertemporal choice, with a focus on those that utilize functional MRI (fMRI). We consider the neural bases for the most common economic models of intertemporal choice and examine whether these models require neural processes that are common or distinct across types of decision making. Considered as a whole, current research points to potentially distinct contributions from brain systems associated with valuation and with prospective thought, which may be reflected in separable foci in posterior cingulate cortex. Based on open questions in the field, we suggest two core goals for future research: identifying aspects of valuation that are unique to intertemporal choice and evaluating direct or indirect interactions between delay and prize magnitude. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parallel recovery in a bilingual aphasic: A neurolinguistic and fMRI study.

In bilingual aphasics, the neural correlates of rehabilitation benefits and their generalization across languages are still scarcely understood. The authors present the case of a highly proficient bilingual woman (Flemish, L1/Italian, L2) with chronic aphasia who, in the presence of the same pattern of impairment in both languages, showed parallel recovery in both languages after long-term rehabilitation therapy in L2. The authors postulated that this recovery was due to the engagement of the same neural substrates. To confirm this the authors used an event-related functional magnetic resonance imaging (fMRI) paradigm to explore cortical activation during an overt picture naming task, performed in both Flemish and Italian once before and once after 2 weeks of training in L2. Behaviorally, the patient showed complete recovery of both languages. The fMRI results indicated that the same cerebral regions were recruited for both languages before and after training. Increasing activations were observed perilesionally and in homologous contralesional areas. Our data, in agreement with previous results of fMRI studies in healthy bilinguals, indicate a promising direction for future research on the neural mechanisms associated with recovery in bilingual aphasics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Understanding the dorsal and ventral systems of the human cerebral cortex: Beyond dichotomies.

Traditionally, characterizations of the macrolevel functional organization of the human cerebral cortex have focused on the left and right cerebral hemispheres. However, the idea of left brain versus right brain functions has been shown to be an oversimplification. We argue here that a top–bottom divide, rather than a left–right divide, is a more fruitful way to organize human cortical brain functions. However, current characterizations of the functions of the dorsal (top) and ventral (bottom) systems have rested on dichotomies, namely where versus what and how versus what. We propose that characterizing information-processing systems leads to a better macrolevel organization of cortical function; specifically, we hypothesize that the dorsal system is driven by expectations and processes sequences, relations, and movement, whereas the ventral system categorizes stimuli in parallel, focuses on individual events, and processes object properties (such as shape in vision and pitch in audition). To test this hypothesis, we reviewed over 100 relevant studies in the human neuroimaging and neuropsychological literatures and coded them relative to 11 variables, some of which characterized our hypothesis and some of which characterized the previous dichotomies. The results of forward stepwise logistic regressions supported our characterization of the 2 systems and showed that this model predicted the empirical findings better than either the traditional dichotomies or a left–right difference. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.     

Innovative clinical assessment technologies: Challenges and opportunities in neuroimaging.

The authors review the reasons for the contrast between the remarkable advances that hemodynamic and electromagnetic imaging of the human brain appear capable of delivering in clinical practice in psychology and their very limited penetration into practice to date. Both the heritages of the relevant technologies and the historical orientation of clinical psychology away from biological phenomena are factors. Discussion of some technical aspects and prospects of these methods and recommendations for facilitating clinical use are provided, with an emphasis on fostering the participation of and contribution by practicing clinical psychologists and professionals in related fields lacking a strong grounding in biological measurement. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Clinical Parameters and Epigenetic Biomarkers of Plaque Vulnerability in Patients with Carotid Stenosis

Atheromatous disease is the first cause of death and dependency in developed countries and carotid artery atherosclerosis is one of the main causes of severe ischaemic strokes. Current management strategies are mainly based on the degree of stenosis and patient selection has limited accuracy. This information could be complemented by the identification of biomarkers of plaque vulnerability, which would permit patients at greater and lesser risk of stroke to be distinguished, thus enabling a better selection of patients for surgical or intensive medical treatment. Although several circulating protein-based biomarkers with significance for both the diagnosis of carotid artery disease and its prognosis have been identified, at present, none have been clinically implemented. This review focuses especially on the most relevant clinical parameters to take into account in routine clinical practice and summarises the most up-to-date data on epigenetic biomarkers of carotid atherosclerosis and plaque vulnerability.     

Association of DNA Methylation of the NLRP3 Gene with Changes in Cortical Thickness in Major Depressive Disorder

The Nod-like receptor pyrin containing 3 (NLRP3) inflammasome has been reported to be a convergent point linking the peripheral immune response induced by psychological stress and neuroinflammatory processes in the brain. We aimed to identify differences in the methylation profiles of the NLRP3 gene between major depressive disorder (MDD) patients and healthy controls (HCs). We also investigated the correlation of the methylation score of loci in NLRP3 with cortical thickness in the MDD group using magnetic resonance imaging (MRI) data. A total of 220 patients with MDD and 82 HCs were included in the study, and genome-wide DNA methylation profiling of the NLRP3 gene was performed. Among the total sample, 88 patients with MDD and 74 HCs underwent T1-weighted structural MRI and were included in the neuroimaging–methylation analysis. We identified five significant differentially methylated positions (DMPs) in NLRP3. In the MDD group, the methylation scores of cg18793688 and cg09418290 showed significant positive or negative correlations with cortical thickness in the occipital, parietal, temporal, and frontal regions, which showed significant differences in cortical thickness between the MDD and HC groups. Our findings suggest that NLRP3 DNA methylation may predispose to depression-related brain structural changes by increasing NLRP3 inflammasome-related neuroinflammatory processes in MDD.     

Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder.

Mindfulness-based stress reduction (MBSR) is an established program shown to reduce symptoms of stress, anxiety, and depression. MBSR is believed to alter emotional responding by modifying cognitive–affective processes. Given that social anxiety disorder (SAD) is characterized by emotional and attentional biases as well as distorted negative self-beliefs, we examined MBSR-related changes in the brain–behavior indices of emotional reactivity and regulation of negative self-beliefs in patients with SAD. Sixteen patients underwent functional MRI while reacting to negative self-beliefs and while regulating negative emotions using 2 types of attention deployment emotion regulation—breath-focused attention and distraction-focused attention. Post-MBSR, 14 patients completed neuroimaging assessments. Compared with baseline, MBSR completers showed improvement in anxiety and depression symptoms and self-esteem. During the breath-focused attention task (but not the distraction-focused attention task), they also showed (a) decreased negative emotion experience, (b) reduced amygdala activity, and (c) increased activity in brain regions implicated in attentional deployment. MBSR training in patients with SAD may reduce emotional reactivity while enhancing emotion regulation. These changes might facilitate reduction in SAD-related avoidance behaviors, clinical symptoms, and automatic emotional reactivity to negative self-beliefs in adults with SAD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Contribution of neuroimaging in the diagnosis of brain disorders: Recent findings and future applications

Brain disorders including neurological and psychiatric disorders have become a major global public health issue due to their high prevalence and burden. However, elucidating ultimate causes and improving strategies for diagnosis and treatment of these disorders remain challenging due to limited accessibility to living human brain. In addition, there has been a great need for robust biomarkers for them at a very early stage. Neuroimaging technologies have demonstrated the potential in investigating the pathophysiology and developing the biomarkers. The current review discusses the potential diagnostic applications of neuroimaging in brain disorders. We summarized major findings in recent neuroimaging meta‐analyses, which could be used as future biomarkers, in Alzheimer's disease, Parkinson's disease, central nervous system inflammation, depression, and schizophrenia. New possibilities of novel imaging techniques were also demonstrated. Finally, future directions for the imaging‐based diagnosis were suggested. In spite of promising results from preliminary studies and rapid technological advances, further studies on the reliability and validity of potential imaging biomarkers in larger patient populations and the development of new guidelines for the clinical applications would be required.