e present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.
Individuals with insomnia often report aspects of perfectionism alongside symptoms of anxiety and depression. However, there has been limited examination of these factors together. The current study investigated whether individuals with insomnia report increased perfectionism compared to normal-sleepers. Further, the mediating role of anxiety and depression was examined. Participants were 39 individuals with DSM-5 defined Insomnia Disorder, and 39 normal-sleepers, who completed two measures of multidimensional perfectionism and the Hospital Anxiety and Depression Scale. Results demonstrated that, compared to normal-sleepers, individuals with insomnia display increased perfectionistic traits of: concern over mistakes, doubts about action, and parental criticism. In addition, these differences were partiality mediated by symptoms of anxiety, but not depression. Our findings highlight the significance of treating symptoms of anxiety with the prospect of alleviating negative thoughts concerning one's mistakes, doubts about action, and perception of parental criticism, which may contribute to insomnia.
Recovery after stroke relates tightly to the white matter integrity. Currently, the main methodology for non-invasive white matter integrity assessment is diffusion-weighted magnetic resonance imaging (DW-MRI), a state-of-the-art approach which is, however, prone to multiple limitations. Using DW-MRI, it was demonstrated that many pathways including corticospinal tract (CST) and corpus callosum contribute to structural brain reserve after stroke, but only a few of these tracts were found to be useful in the clinical practice. The most widely known measure is an asymmetry of the fractional anisotropy (FA) in CST at the level of the internal capsule, which could be used for predicting motor recovery in acute stroke. Recently, a new complementary motor component of the structural reserve, the so-called alternate motor fibers (AMFs), was proposed for motor recovery prognosis in stroke patients, and it was even reported to correlate with the effect of the transcranial direct current stimulation in chronic stroke. Here, we would like to point out a possible additional sensory interpretation of the AMF that appears plausible after taking into account technical limitations of DW-MRI approach, which may potentially give rise to different interpretations of the same results.
Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction.
Когнитивный контроль включает в себя поддержание специфических процессов, связанных со вниманием, и неспецифическую регуляцию моторного порога. Ошибки при выполнении задачи могут быть двух типов: они либо связаны со сбоями внимания и неопределенностью, либо с нарушением моторного порога. Совершение ошибок запускает адаптивные перестройки в мозге, приводя либо к улучшению обработки стимула, либо к повышению моторного порога. Проведено два исследования с использованием слуховой конденсационной задачи, создающей высокую нагрузку на внимание. Анализировали осцилляции ЭЭГ в тета, альфа и бета диапазонах.
Эксперимент 1. Исследовали адаптивные перестройки как следствие совершения ошибок. После ошибок наблюдалось усиление фронтальных тета-осцилляций средней линии (ФТСЛ), а также более существенное подавление альфа осцилляций в теменных и левых центральных областях. Подавление альфа осцилляций в теменной области коррелировало с успешностью выполнения задачи, подавление альфа осцилляций в левой центральной области коррелировало с замедлением времени реакции после совершения ошибок, а усиление ФТСЛ коррелировало с обоими показателями. В реализациях, следующих за ошибками, подавление альфа осцилляций начиналось раньше, и ответ сопровождался более слабой ФТСЛ активностью, а также более значительным подавлением альфа осцилляций, широко распределенным по скальпу. Полученные результаты указывают, что адаптивные перестройки после ошибок реализуются при участии фронто-медиальной сети мониторинга, теменной системы внимания, и сенсомоторной сети.
Эксперимент 2. Исследовали, может ли время реакции служить признаком, позволяющим разделить реакции с низкими и высокими уровнями внимания и неопределенности. Показано, что ФТСЛ, связанная с ошибками, возникала только при быстрых поведенческих ответах. Позднее подавление альфа-осцилляций после ответа было выражено только для медленных правильных ответов. Полученные данные в совокупности указывают, что время реакции позволяет разделить два типа ответов: быстрые связаны с более высоким уровнем внимания и низкой неопределенностью, а медленные – с низким уровнем внимания и высокой неопределенностью.
As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.
To help us live in the three-dimensional world, our brain integrates incoming spatial information into reference frames, which are based either on our own body (egocentric) or independent from it (allocentric). Such frames, however, may be crucial not only when interacting with the visual world, but also in language comprehension, since even the simplest utterance can be understood from different perspectives. While significant progress has been made in elucidating how linguistic factors, such as pronouns, influence reference frame adoption, the neural underpinnings of this ability are largely unknown. Building on the neural reuse framework, this study tested the hypothesis that reference frame processing in language comprehension involves mechanisms used in navigation and spatial cognition. We recorded EEG activity in 28 healthy volunteers to identify spatiotemporal dynamics in (1) spatial navigation, and (2) a language comprehension task (sentence-picture matching). By decomposing the EEG signal into a set of maximally independent activity patterns, we localised and identified a subset of components which best characterised perspective-taking in both domains. Remarkably, we find individual co-variability across these tasks: people's strategies in spatial navigation are also reflected in their construction of sentential perspective. Furthermore, a distributed network of cortical generators of such strategy-dependent activity responded not only in navigation, but in sentence comprehension. Thus we report, for the first time, evidence for shared brain mechanisms across these two domains - advancing our understanding of language's interaction with other cognitive systems, and the individual differences shaping comprehension. © 2017 Elsevier Inc
A crucial question facing cognitive science concerns the nature of conceptual representations as well as the constraints on the interactions between them. One specific question we address in this paper is what makes cross-representational interplay possible? We offer two distinct theoretical scenarios: according to the first scenario, co-activated knowledge representations interact with the help of an interface established between them via congruent activation in a mediating third-party general cognitive mechanism, e.g., attention. According to the second scenario, co-activated knowledge representations interact due to an overlap between their features, for example when they share a magnitude component. First, we make a case for cross-representational interplay based on grounded and situated theories of cognition. Second, we discuss interface-based interactions between distinct (i.e., non-overlapping) knowledge representations. Third, we discuss how co-activated representations may share their architecture via partial overlap. Finally, we outline constraints regarding the flexibility of these proposed mechanisms.
Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific frequency and in turn modulate ongoing cortical oscillatory activity. This neurotool allows the establishment of a causal link between endogenous oscillatory activity and behavior. Most of the tACS studies have shown online effects of tACS. However, little is known about the underlying action mechanisms of this technique because of the AC-induced artifacts on Electroencephalography (EEG) signals. Here we show a unique approach to investigate online physiological frequency-specific effects of tACS of the primary motor cortex (M1) by using single pulse Transcranial Magnetic Stimulation (TMS) to probe cortical excitability changes. In our setup, the TMS coil is placed over the tACS electrode while Motor Evoked Potentials (MEPs) are collected to test the effects of the ongoing M1-tACS. So far, this approach has mainly been used to study the visual and motor systems. However, the current tACS-TMS setup can pave the way for future investigations of cognitive functions. Therefore, we provide a step-by-step manual and video guidelines for the procedure.