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Alzheimer's disease dementia (ADD) is the most diffuse neurodegenerative disorder belonging to mild cognitive impairment (MCI) and dementia in old persons. This disease is provoked by an abnormal accumulation of amyloid-beta and tauopathy proteins in the brain. Very recently, the first disease-modifying drug has been licensed with reserve (i.e., Aducanumab). Therefore, there is a need to identify and use biomarkers probing the neurophysiological underpinnings of human cognitive functions to test the clinical efficacy of that drug. In this regard, event-related electroencephalographic potentials (ERPs) and oscillations (EROs) are promising candidates. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association and Global Brain Consortium reviewed the field literature on the effects of the most used symptomatic drug against ADD (i.e., Acetylcholinesterase inhibitors) on ERPs and EROs in ADD patients with MCI and dementia at the group level. The most convincing results were found in ADD patients. In those patients, Acetylcholinesterase inhibitors partially normalized ERP P300 peak latency and amplitude in oddball paradigms using visual stimuli. In these same paradigms, those drugs partially normalize ERO phase-locking at the theta band (4–7 Hz) and spectral coherence between electrode pairs at the gamma (around 40 Hz) band. These results are of great interest and may motivate multicentric, double-blind, randomized, and placebo-controlled clinical trials in MCI and ADD patients for final cross-validation. United States Department of Health & Human Services National Institutes of Health (NIH) - USA

Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean ± SD: 31.5 ± 7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R2day1 = 55.9%, 95% CI: 22.6–79%, P day1 = 0.91/0.82) recovery definitions remained significant after leave-one-out cross-validation. Identified prefrontal neural predictors might propel the future development of fMRI markers for clinical decision making, which could lead to increased response rates and adherence during acute phase treatment periods. Moreover, this study underscores the importance of the amPFC in depression recovery.

Highlights • The right basolateral amygdala, the hippocampus and the right insular cortex are important nodes in obstructive sleep apnea (OSA). • Functional characterization of these regions suggested associated dysfunction of emotional, sensory, and limbic processes in OSA. • Connectivity analysis demonstrated that these regions are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus., Obstructive sleep apnea (OSA) is a common multisystem chronic disorder. Functional and structural neuroimaging has been widely applied in patients with OSA, but these studies have often yielded diverse results. The present quantitative meta-analysis aims to identify consistent patterns of abnormal activation and grey matter loss in OSA across studies. We used PubMed to retrieve task/resting-state functional magnetic resonance imaging and voxel-based morphometry studies. Stereotactic data were extracted from fifteen studies, and subsequently tested for convergence using activation likelihood estimation. We found convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula. Functional characterization of these regions using the BrainMap database suggested associated dysfunction of emotional, sensory, and limbic processes. Assessment of task-based co-activation patterns furthermore indicated that the two regions obtained from the meta-analysis are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus. Taken together, our findings highlight the role of right amygdala, hippocampus and insula in the abnormal emotional and sensory processing in OSA.

Bipolar disorder (BPD) is associated with altered regional brain function during the performance of cognitive tasks. The relative contribution of genetic and environmental risk factors for BPD to these changes has not yet been quantified. We sought to address this issue in a functional neuroimaging study of people who varied in their risk for BPD. Functional magnetic resonance imaging was used to study 124 subjects (29 twin and 9 sibling pairs with at least one member with BPD, and 24 healthy twin pairs) performing a working memory task. We assessed the influence of risk for BPD on regional brain function during the task in a two stage process. Firstly, we identified areas where there were group differences in activation. Secondly, we estimated the heritability and phenotypic correlation of activation and BPD using genetic modeling. BPD was associated with increased activation in the anterior cingulate, orbitofrontal, medial prefrontal, and left precentral cortices, and in the precuneus. Within these regions, activation in the orbitofrontal cortex rendered the most significant heritability estimate (h(2)=0.40), and was significantly correlated with BPD phenotype (rph=0.29). A moderate proportion of the genetic influences (rg=0.69) acting on both BPD and on the degree of orbitofrontal activation were shared. These findings suggest that genetic factors that confer vulnerability to BPD alter brain function in BPD.

In the vast majority of electrophysiological studies on cognition, participants are only measured once during a single experimental session. The dearth of studies on test-retest reliability in magnetoencephalography (MEG) within and across experimental sessions is a preventing factor for longitudinal designs, imaging genetics studies, and clinical applications. From the recorded signals, it is not straightforward to draw robust and steady indices of brain activity that could directly be used in exploring behavioral effects or genetic associations. To study the variations in markers associated with cognitive functions, we extracted three event-related field (ERF) features from time-locked global field power (GFP) epochs using MEG while participants were performing a numerical N-back task in four consecutive measurements conducted during two different days separated by two weeks. We demonstrate that the latency of the MI70, a neural correlate associated with cognitive functions such as working memory, was a stable parameter and did not show significant variations over time. In addition, the M170 peak amplitude and the mean amplitude of late positive component (LPP) also expressed moderate-to-strong reliability across multiple measures over time over many sensor spaces and between participants. The M170 amplitude varied more significantly between the measurements in some conditions but showed consistency over the participants over time. In addition we demonstrated significant correlation with the M170 and LPP parameters and cognitive load. The results are in line with the literature showing less within-subject fluctuation for the latency parameters and more consistency in between-subject comparisons for amplitude based features. The within-subject consistency was apparent also with longer delays between the measurements. We suggest that with a few limitations the ERF features show sufficient reliability and stability for longitudinal research designs and clinical applications for cognitive functions in single as well as cross-subject designs. (C) 2016 Elsevier Inc. All rights reserved.

Dissecting trait neurobiological abnormalities in bipolar disorder ( BD) from those characterizing episodes of mood disturbance will help elucidate the aetiopathogenesis of the illness. This selective review highlights the immunological, neuroendocrinological, molecular biological and neuroimaging abnormalities characteristic of BD, with a focus on those likely to reflect trait abnormalities by virtue of their presence in euthymic patients or in unaffected relatives of patients at high genetic liability for illness. Trait neurobiological abnormalities of BD include heightened pro-inflammatory function and hypothalamic-pituitary-adrenal axis dysfunction. Dysfunction in the intracellular signal transduction pathway is indicated by elevated protein kinase A activity and altered intracellular calcium signalling. Consistent neuroimaging abnormalities include the presence of ventricular enlargement and white matter abnormalities in patients with BD, which may represent intermediate phenotypes of illness. In addition, spectroscopy studies indicate reduced prefrontal cerebral N-acetylaspartate and phosphomonoester concentrations. Functional neuroimaging studies of euthymic patients implicate inherently impaired neural networks subserving emotional regulation, including anterior limbic, ventral and dorsal prefrontal regions. Despite heterogeneous samples and conflicting findings pervading the literature, there is accumulating evidence for the existence of neurobiological trait abnormalities in BD at various scales of investigation. The aetiopathogenesis of BD will be better elucidated by future clinical research studies, which investigate larger and more homogenous samples and employ a longitudinal design to dissect neurobiological abnormalities that are underlying traits of the illness from those related to episodes of mood exacerbation or pharmacological treatment. Molecular Psychiatry (2009) 14, 833-846; doi:10.1038/mp.2009.39; published online 19 May 2009

This is a pilot study describing event-related oscillations in patients with Alzheimer-type dementia (AD). Theta responses of 22 mild probable AD subjects according to NINCDS-ADRDA criteria (11 non-treated, 11 treated by cholinesterase inhibitors), and 20 healthy elderly controls were analyzed by using the conventional visual oddball paradigm. We aimed to compare theta responses of the three groups in a range between 4-7 Hz at the frontal electrodes. At F-3 location, theta responses of healthy subjects were phase locked to stimulation and theta oscillatory responses of non-treated Alzheimer patients showed weaker phase-locking, i.e. average of Z-transformed means of correlation coefficients between single trials was closer to zero. In treated AD patients, phase-locking following target stimulation was two times higher in comparison to the responses of non-treated patients. The results indicate that the phase-locking of theta oscillations at F-3 in the treated patients is as strong as the control subjects. The F-4 theta responses were not statistically significant between the groups. Our findings imply that the theta responses at F3 location are highly unstable in comparison to F4 in non-treated mild AD patients and cholinergic agents may modulate event-related theta oscillatory activities in the frontal regions. (c) 2006 Elsevier B.V. All rights reserved.

Functional MRI is widely used to address basic and clinical neuroscience questions. In the key domains of fMRI experiments, i.e. acquisition, processing and analysis, and interpretation of data, developments are ongoing. The main issues are sensitivity for changes in fMRI signal that are associated with brain function, and the design of tasks with which brain functions are invoked. In this paper we address these issues, in terms of strengths, weaknesses and future developments. Acquisition of data is commonly achieved with techniques that measure blood oxygen level-dependent (BOLD) signal changes. Although the mechanisms that affect BOLD signal are complex and not well understood, fMRI yields results that agree with known functional topography. Sensitivity for task-related brain activity is expected to benefit from technological advances in acquisition, i.e. SENSE or parallel imaging, and higher field scanners (3 T). Data analysis is geared towards modelling sources of signal variation, i.e. reducing noise in the data time-series, and the cerebrovascular response to task-related changes in neuronal activity. Analytical algorithms such as connectivity and component analysis contribute to the extraction of meaningful information from fMRI datasets. The choice of tasks, and consequently of the statistical evaluation procedures, is best guided by the specific questions that are formulated a priori. The future is expected to bring more sophisticated questions, and tasks that allow for accurate modelling of involved brain functions. An example of a hypothesis-driven experiment is presented, where we investigated whether practise of a working memory task caused a shift in the neuronal representation of working memory or not. (C) 2002 Elsevier Science B.V./ECNP All rights reserved.

Understanding the cognitive sequela of repeated cocaine use is a growing area of research and is crucial to the development of cognitive models of addiction. We systematically reviewed all available placebo-controlled and case-controlled studies on the acute and long-term effects of cocaine on cognitive functioning. In order to compare the magnitude of cognitive effects across cognitive domains we conducted several meta-analyses on a subset of data from long-term effect studies. Studies on acute cocaine administration suggest enhancement of response inhibition and psychomotor speed, while all other domains appear to be unaffected or not investigated adequately. Long-term effects of cocaine show a wide array of deteriorated cognitive functions, indicating that long term cocaine use is characterized by a general cognitive impairment across functions, rather than by specific cognitive deficits. Literature on long-term cocaine effects is more substantial than literature on acute effects. This comprehensive review outlines possible dissociations and similarities of acute vs. long-term cocaine effects in the human brain. Atherosclerosis after cocaine exposure may underlie cognitive dysfunction, suggesting involvement of multiple brain areas. Acute drug studies are important to the future development of addiction models. (C) 2013 Elsevier Ltd. All rights reserved.

[EN] This paper presents two applications of Independent Component Analysis Mixture Modeling (ICAMM) for the classification and prediction of data. The first one of these extensions is Sequential ICAMM (SICAMM), an ICAMM structure that takes into account the sequential dependence in the feature record. This algorithm can be used to classify input observations in a given set of mutually-exclusive classes. The performance of SICAMM is tested with simulations and compared against that of the base ICAMM algorithm and of a Dynamic Bayesian Network (DBN). All three methods are also used to classify real electroencephalographic (EEG) signals to compute hypnograms, a clinical tool used to help in the diagnosis of sleep disorders. The second extension of ICAMM is PREDICAMM, an estimation algorithm that makes use of the ICAMM parameters in order to reconstruct missing samples from a set of data. This predictor is used to reconstruct real EEG data from a working memory experiment, and its performance is compared to that of a classical predictor for EEG signals: sphere splines. Prediction performance is measured with four error indicators: signal-to-interference ratio, KullbackLeibler divergence, correlation, and mean structural similarity index. Both extensions of the base ICAMM algorithm have achieved a higher performance than other methods, This work has been supported by Universitat Politècnica de Valencia under grant 20130072, Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006; and Spanish Administration and European Union FEDER Programme under grant TEC2011-23403 01/01/2012. The PSG signals and annotated hypnograms were provided by the Electroencephalography Department of Hospital Universitario La Fe, Valencia, Spain

This paper presents a new application of independent component analysis mixture modeling (ICAMM) for prediction of electroencephalographic (EEG) signals. Demonstrations in prediction of missing EEG data in a working memory task using classic methods and an ICAMM-based algorithm are included. The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. The results show that the ICAMM-based algorithm outperforms the classical spherical splines method which is commonly used in EEG signal processing. Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated., This work has been supported by Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006

Background: Visual delta event-related (ERO) and evoked oscillations (EO) of Alzheimer patients (AD) are different than healthy. In the present study, the analysis is extented to include auditory ERO and EO in AD. The rationale is to reveal whether the auditory ERO delta responses are also reduced, and whether this is a general phenomenon in Alzheimer patients upon applying stimuli with cognitive load. Methods.: Thirty-four mild AD subjects 117 de-novo and 17 medicated (cholinergic)l and seventeen healthy controls were included. Auditory oddball paradigm and sensory auditory stimuli were applied to the subjects. Oscillatory responses were analyzed by measuring maximum amplitudes in delta frequency range (0.5-3.5 Hz). Results: Auditory delta ERO (0.5-3.5 Hz) responses of healthy controls were higher than either de-novo AD or medicated AD group, without a difference between two AD subgroups. Furthermore, the auditory EO after presentation of tone bursts yielded no group difference. Conclusion: Our findings imply that delta ERO is highly unstable in AD patients in comparison to age-matched healthy controls only during the cognitive paradigm. Our results favor the hypothesis that neural delta networks are activated during cognitive tasks and that the reduced delta response is a general phenomenon in AD, due to cognitive impairment.

The analysis of the functional correlates of "brain oscillations" has become an important branch of neuroscience. Although research on the functional correlates of brain oscillation has progressed to a high level, studies on cognitive disorders are rare and mainly limited to schizophrenia patients. The present review includes the results of the changes in brain oscillations in patients with Alzheimer's, schizophrenia, bipolar disorders, mild cognitive impairment, attention-deficit hyperactivity disorder (ADHD), alcoholism and those with genetic disorders. Furthermore, the effects of pharmaca and the influence of neurotransmitters in patients with cognitive disorders are also reviewed. Following the review, a short synopsis is given related to the analysis of brain oscillations. (c) 2008 Elsevier B.V. All rights reserved.

Background and purpose: Alzheimer type of dementia (AD) is the most common neuropsychiatric morbidity in elderly individuals. Event-related oscillations (ERO) provide an useful tool for detecting subtle abnormalities of cognitive processes with high temporal resolution. Methods: In the present report, event-related oscillations of patients with AD were analyzed by using a visual oddball paradigm. A total of 22 mild probable AD subjects according to NINCDS-ADRDA criteria and 20 age-, gender-, and education-matched healthy control subjects were compared. AD group consisted from 11 untreated patients and 11 patients treated with cholinesterase inhibitor. Oscillatory responses were recorded from 13 scalp electrodes. Results: Significant differences in delta frequency range were seen between the groups by using repeated measures of ANOVA analysis [F(9.120) = 2.228; P = 0.022]. Post-hoc analyses using Wilcoxon test showed that at mid- and left central regions, (Cz, C3) peak amplitudes of delta responses of healthy subjects were significantly higher than either group. Also cholinesterase inhibitors did not have effect on delta oscillatory responses. Conclusions: Our findings imply that the delta oscillatory responses at central locations are highly instable in mild probable AD patients regardless of treatment when compared to the healthy aged controls. This study supports the importance of oscillatory event-related potentials for investigating AD brain dynamics.

Functional MRI is widely used to address basic and clinical neuroscience questions. In the key domains of fMRI experiments, i.e. acquisition, processing and analysis, and interpretation of data, developments are ongoing. The main issues are sensitivity for changes in fMRI signal that are associated with brain function, and the design of tasks with which brain functions are invoked. In this paper we address these issues, in terms of strengths, weaknesses and future developments. Acquisition of data is commonly achieved with techniques that measure blood oxygen level-dependent (BOLD) signal changes. Although the mechanisms that affect BOLD signal are complex and not well understood, fMRI yields results that agree with known functional topography. Sensitivity for task-related brain activity is expected to benefit from technological advances in acquisition, i.e. SENSE or parallel imaging, and higher field scanners (3 T). Data analysis is geared towards modelling sources of signal variation, i.e. reducing noise in the data time-series, and the cerebrovascular response to task-related changes in neuronal activity. Analytical algorithms such as connectivity and component analysis contribute to the extraction of meaningful information from fMRI datasets. The choice of tasks, and consequently of the statistical evaluation procedures, is best guided by the specific questions that are formulated a priori. The future is expected to bring more sophisticated questions, and tasks that allow for accurate modelling of involved brain functions. An example of a hypothesis-driven experiment is presented, where we investigated whether practise of a working memory task caused a shift in the neuronal representation of working memory or not. (C) 2002 Elsevier Science B.V./ECNP All rights reserved.