Andreea Oliviana Diaconescu

Increased Cerebral Metabolism After 1 Year of Deep Brain Stimulation in Alzheimer Disease

BACKGROUND The importance of developing unique, neural circuitry-based treatments for the cognitive and neuropsychiatric symptoms of Alzheimer disease (AD) was the impetus for a phase I study of deep brain stimulation (DBS) in patients with AD that targeted the fornix. OBJECTIVE To test the hypotheses that DBS would increase cerebral glucose metabolism in cortical and hippocampal circuits and that increased metabolism would be correlated with better clinical outcomes. DESIGN Open-label trial. SETTING Academic medical center. PATIENTS A total of 5 patients with mild, probable AD (1 woman and 4 men, with a mean [SD] age of 62.6 [4.2] years). INTERVENTION Deep brain stimulation of the fornix. MAIN OUTCOME MEASURES All patients underwent clinical follow-up and high-resolution positron emission tomography studies of cerebral glucose metabolism after 1 year of DBS. RESULTS Functional connectivity analyses revealed that 1 year of DBS increased cerebral glucose metabolism in 2 orthogonal networks: a frontal-temporal-parietal-striatal-thalamic network and a frontal-temporal-parietal-occipital-hippocampal network. In similar cortical regions, higher baseline metabolism prior to DBS and increased metabolism after 1 year of DBS were correlated with better outcomes in global cognition, memory, and quality of life. CONCLUSIONS Increased connectivity after 1 year of DBS is observed, which is in contrast to the decreased connectivity observed over the course of AD. The persistent cortical metabolic increases after 1 year of DBS were associated with better clinical outcomes in this patient sample and are greater in magnitude and more extensive in the effects on cortical circuitry compared with the effects reported for pharmacotherapy over 1 year in AD.

Translational Perspectives for Computational Neuroimaging.

Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18–72) and one magnetoencephalography (n = 31, ages 20–75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.

Distinct functional networks associated with improvement of affective symptoms and cognitive function during citalopram treatment in geriatric depression.

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. This study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting positron emission tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after 8 weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel‐wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex, and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical‐limbic‐frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal‐parietal‐frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimers dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression. Hum Brain Mapp, 2010.

Aberrant Effective Connectivity in Schizophrenia Patients during Appetitive Conditioning

It has recently been suggested that schizophrenia involves dysfunction in brain connectivity at a neural level, and a dysfunction in reward processing at a behavioral level. The purpose of the present study was to link these two levels of analyses by examining effective connectivity patterns between brain regions mediating reward learning in patients with schizophrenia and healthy, age-matched controls. To this aim, we used functional magnetic resonance imaging and galvanic skin recordings (GSR) while patients and controls performed an appetitive conditioning experiment with visual cues as the conditioned (CS) stimuli, and monetary reward as the appetitive unconditioned stimulus (US). Based on explicit stimulus contingency ratings, conditioning occurred in both groups; however, based on implicit, physiological GSR measures, patients failed to show differences between CS+ and CS− conditions. Healthy controls exhibited increased blood-oxygen-level dependent (BOLD) activity across striatal, hippocampal, and prefrontal regions and increased effective connectivity from the ventral striatum to the orbitofrontal cortex (OFC BA 11) in the CS+ compared to the CS− condition. Compared to controls, patients showed increased BOLD activity across a similar network of brain regions, and increased effective connectivity from the striatum to hippocampus and prefrontal regions in the CS− compared to the CS+ condition. The findings of increased BOLD activity and effective connectivity in response to the CS− in patients with schizophrenia offer insight into the aberrant assignment of motivational salience to non-reinforced stimuli during conditioning that is thought to accompany schizophrenia.

Dopamine-induced changes in neural network patterns supporting aversive conditioning

The aim of the present paper is to assess the effects of altered dopamine (DA) transmission on the functional connectivity among brain regions mediating aversive conditioning in humans. To this aim, we analyzed a previous published data set from a double-blind design combined with functional magnetic resonance imaging (fMRI) recordings in which healthy volunteers were randomly assigned to one of three drug groups: amphetamine (an indirect DA agonist), haloperidol (DA D2 receptor antagonist), and placebo. Participants were exposed to an aversive classical conditioning paradigm using cutaneous electrical stimulation as the unconditioned stimulus (US), and visual cues as the conditioned stimuli (CS) where one colour (CS+) was followed by the US in 33% of the trials and another colour (CS-) had no consequences. All participants reported awareness of stimulus contingencies. Group analysis of fMRI data revealed that the left ventral striatum (VS) and amygdala activated in response to the CS+ in all the three groups. Because of their activation patterns and documented involvement in aversive conditioning, both regions were used as seeds in the functional connectivity analysis. To constrain the functional networks obtained to relate to the conditioned response, we also correlated seed activity with the Galvanic Skin Response (GSR). In the placebo group, the right ventral tegmental area/substantia nigra (VTA/SN), bilateral caudate, right parahippocampal gyrus, left inferior parietal lobule (IPL), bilateral postcentral gyrus, bilateral middle frontal (BA 46), orbitofrontal, and ventromedial prefrontal cortices (PFC, BA 10/11) correlated with the VS and amygdala seeds in response to the CS+ compared to the CS-. Enhancing dopamine transmission via amphetamine was associated with reduced task differences and significant functional connectivity for both CS+ and CS- conditions between the left VS seed and regions modulated by DA, such as the left VTA/SN, right caudate, left amygdala, left middle frontal gyrus (BA 46), and bilateral ventromedial PFC (BA 10). Blocking dopamine transmission via haloperidol was associated with significant functional connectivity across an alternate network of regions including the left amygdala seed and the right insula, the left ACC (BA 24/32), bilateral IPL (BA 40), precuneus (BA 7), post-central gyrus, middle frontal gyrus (BA 46), and supplementary motor area (SMA, BA 6) to the CS+ versus the CS-. These data provide insight into the distinct effects of DA agents on the functional connectivity between striatal, limbic, and prefrontal areas.

The PhysIO toolbox for modeling physiological noise in fMRI data

BACKGROUND Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Hierarchical prediction errors in midbrain and septum during social learning

Abstract Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser’s intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser’s trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support ‘theory of mind’, but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs (‘expected uncertainty’) about the adviser’s fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others.

Inversion of hierarchical Bayesian models using Gaussian processes

Over the past decade, computational approaches to neuroimaging have increasingly made use of hierarchical Bayesian models (HBMs), either for inferring on physiological mechanisms underlying fMRI data (e.g., dynamic causal modelling, DCM) or for deriving computational trajectories (from behavioural data) which serve as regressors in general linear models. However, an unresolved problem is that standard methods for inverting the hierarchical Bayesian model are either very slow, e.g. Markov Chain Monte Carlo Methods (MCMC), or are vulnerable to local minima in non-convex optimisation problems, such as variational Bayes (VB). This article considers Gaussian process optimisation (GPO) as an alternative approach for global optimisation of sufficiently smooth and efficiently evaluable objective functions. GPO avoids being trapped in local extrema and can be computationally much more efficient than MCMC. Here, we examine the benefits of GPO for inverting HBMs commonly used in neuroimaging, including DCM for fMRI and the Hierarchical Gaussian Filter (HGF). Importantly, to achieve computational efficiency despite high-dimensional optimisation problems, we introduce a novel combination of GPO and local gradient-based search methods. The utility of this GPO implementation for DCM and HGF is evaluated against MCMC and VB, using both synthetic data from simulations and empirical data. Our results demonstrate that GPO provides parameter estimates with equivalent or better accuracy than the other techniques, but at a fraction of the computational cost required for MCMC. We anticipate that GPO will prove useful for robust and efficient inversion of high-dimensional and nonlinear models of neuroimaging data.

Modality-dependent what and where preparatory processes in auditory and visual systems

The present study examined the modality specificity and spatio-temporal dynamics of “what” and “where” preparatory processes in anticipation of auditory and visual targets using ERPs and a cue–target paradigm. Participants were presented with an auditory (Experiment 1) or a visual (Experiment 2) cue that signaled them to attend to the identity or location of an upcoming auditory or visual target. In both experiments, participants responded faster to the location compared to the identity conditions. Multivariate spatio-temporal partial least square (ST-PLS) analysis of the scalp-recorded data revealed supramodal “where” preparatory processes between 300–600 msec and 600–1200 msec at central and posterior parietal electrode sites in anticipation of both auditory and visual targets. Furthermore, preparation for pitch processing was captured at modality-specific temporal regions between 300 and 700 msec, and preparation for shape processing was detected at occipital electrode sites between 700 and 1150 msec. The spatio-temporal patterns noted above were replicated when a visual cue signaled the upcoming response (Experiment 2). Pitch or shape preparation exhibited modality-dependent spatio-temporal patterns, whereas preparation for target localization was associated with larger amplitude deflections at multimodal, centro-parietal sites preceding both auditory and visual targets. Using a novel paradigm, the study supports the notion of a division of labor in the auditory and visual pathways following both auditory and visual cues that signal identity or location response preparation to upcoming auditory or visual targets.