Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training.

fMRI neurofeedback using autobiographical memory recall to upregulate the amygdala is associated with resting-state functional connectivity (rsFC) changes between the amygdala and the salience and default mode networks (SN and DMN, respectively). We hypothesize the existence of anatomical circuits underlying these rsFC changes. Using a cross-species brain parcellation, we identified in non-human primates locations homologous to the regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala.

Impact of maternal depressive symptoms on offspring executive functions: a systematic review.

Objective: To conduct a thorough examination of the current understanding of the effect of maternal depression exposure on the executive functions (EFs) of offspring.

Methods: Following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) statement, a comprehensive search for peer-reviewed cohort studies was performed on the MEDLINE (via PubMed), ScienceDirect, LILACS, PsycINFO, and SciELO databases. Study quality was assessed using the National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.

Specific Patterns of Endogenous Functional Connectivity Are Associated With Harm Avoidance in Obsessive-Compulsive Disorder.

Background: Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance.

Multimodal resting-state connectivity predicts affective neurofeedback performance.

Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques.

Anatomical and functional connectivity support the existence of a salience network node within the caudal ventrolateral prefrontal cortex.

Three large-scale networks are considered essential to cognitive flexibility: the ventral and dorsal attention (VANet and DANet) and salience (SNet) networks. The ventrolateral prefrontal cortex (vlPFC) is a known component of the VANet and DANet, but there is a gap in the current knowledge regarding its involvement in the SNet. Herein, we used a translational and multimodal approach to demonstrate the existence of a SNet node within the vlPFC.

Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment.

Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients.

A prefrontal network integrates preferences for advance information about uncertain rewards and punishments.

Humans and animals can be strongly motivated to seek information to resolve uncertainty about rewards and punishments. In particular, despite its clinical and societal relevance, very little is known about information seeking about punishments. We show that attitudes toward information about punishments and rewards are distinct and separable at both behavioral and neuronal levels.

Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges.

Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments.

Closed-loop neurostimulation for affective symptoms and disorders: An overview.

Affective and anxiety disorders are the most prevalent and incident psychiatric disorders worldwide. Therapeutic approaches to these disorders using non-invasive brain stimulation (NIBS) and analogous techniques have been extensively investigated. In this paper, we discuss the combination of NIBS and neurofeedback in closed-loop setups and its application for affective symptoms and disorders.

Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices.

Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from standard care (i.e.

Subject-independent decoding of affective states using functional near-infrared spectroscopy.

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients.

Resting-state global EEG connectivity predicts depression and anxiety severity.

There is a recent interest in finding neurophysiological biomarkers which will facilitate the diagnosis and understanding of the neural basis of different psychiatric disorders. In this paper, we evaluated the resting-state global EEG connectivity as a potential biomarker for depressive and anxiety symptoms. For this, we evaluated a population of 119 subjects, including 75 healthy subjects and 44 patients with major depressive disorder.

EEG spectro-temporal amplitude modulation as a measurement of cortical hemodynamics: an EEG-fNIRS study.

Neurovascular coupling provides valuable descriptive information about neural function and communication. In this work, we propose to objectively characterize EEG sub-band modulation in an attempt to compare with local variations of fNIRS hemoglobin concentration. First, full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma.

Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles.

Affective neurofeedback constitutes a suitable approach to control abnormal neural activities associated with psychiatric disorders and might consequently relief symptom severity. However, different aspects of neurofeedback remain unclear, such as its neural basis, the performance variation, the feedback effect, among others. First, we aimed to propose a functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback based on the self-regulation of frontal and occipital networks.

Predicting affective valence using cortical hemodynamic signals.

Ascribing affective valence to stimuli or mental states is a fundamental property of human experiences. Recent neuroimaging meta-analyses favor the workspace hypothesis for the neural underpinning of valence, in which both positive and negative values are encoded by overlapping networks but are associated with different patterns of activity. In the present study, we further explored this framework using functional near-infrared spectroscopy (fNIRS) in conjunction with multivariate analyses.

The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures.

Background: Magnetic resonance images (MRI) show detectable anatomical and functional differences between individuals with obsessive-compulsive disorder (OCD) and healthy subjects. Moreover, machine learning techniques have been proposed as tools to identify potential biomarkers and, ultimately, to support clinical diagnosis. However, few studies to date have investigated feature selection (FS) influences in OCD MRI-based classification.

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments.

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments.

Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows.

Functional near-infrared spectroscopy (fNIRS) is currently one of the most promising tools in the neuroscientific research to study brain hemodynamics during naturalistic social communication. The application of fNIRS by studies in this field of knowledge has been widely justified by its strong resilience to motion artifacts, including those that might be generated by communicative head and facial movements. Previous studies have focused on the identification and correction of these artifacts, but a quantification of the differential contribution of common communicative movements on the quality of fNIRS signals is still missing.

Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker.

Alzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD.

EEG spectro-temporal modulation energy: a new feature for automated diagnosis of Alzheimer's disease.

There is recent indication that Alzheimer's disease (AD) can be characterized by atypical modulation of electrophysiological brain activity caused by fibrillar amyloid deposition in specific regions of the brain, such as those related to cognition and memory. In this paper, we propose to objectively characterize EEG sub-band modulation in an attempt to develop an automated noninvasive AD diagnostics tool. First, multi-channel full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma.

Improving Alzheimer's disease diagnosis with machine learning techniques.

There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results.