Resting state functional connectivity patterns associated with ApoE genotype. A MEG study in Mild Cognitive Impairment (MCI)

Alteration of brain communication due to abnormal patterns of synchronization is nowadays one of the most suitable mechanisms for having a better understanding of brain pathologies. Very recently, it has been proved that abnormal changes in both local and long range functional interactions underlie the cognitive deficits associated with different brain disorders. Mild cognitive impairment (MCI) is a state characterized for cognitive dysfunction, such as the memory. The study of the spatial and dynamic alterations in MCI subjects' functional networks could provide important evidences of the brain mechanisms responsible for such impairment.

Altered resting-state EEG source functional connectivity in schizophrenia:the effect of illness duration

Despite the increasing body of evidence supporting the hypothesis of schizophrenia as a disconnection syndrome, studies of resting-state EEG Source Functional Connectivity (EEG-SFC) in people affected by schizophrenia are sparse. The aim of the present study was to investigate resting-state EEG-SFC in 77 stable, medicated patients with schizophrenia (SCZ) compared to 78 healthy volunteers (HV). In order to study the effect of illness duration, SCZ were divided in those with a short duration of disease (SDD; n = 25) and those with a long duration of disease (LDD; n = 52). Resting-state EEG recordings in eyes closed condition were analyzed and lagged phase synchronization (LPS) indices were calculated for each ROI pair in the source-space EEG data. In delta and theta bands, SCZ had greater EEG-SFC than HV; a higher theta band connectivity in frontal regions was observed in LDD compared with SDD. In the alpha band, SCZ showed lower frontal EEG-SFC compared with HV whereas no differences were found between LDD and SDD. In the beta1 band, SCZ had greater EEG-SFC compared with HVs and in the beta2 band, LDD presented lower frontal and parieto-temporal EEG-SFC compared with HV. In the gamma band, SDD had greater connectivity values compared with LDD and HV. This study suggests that resting state brain network connectivity is abnormally organized in schizophrenia, with different patterns for the different EEG frequency components and that EEG can be a powerful tool to further elucidate the complexity of such disordered connectivity.

Resting state morphology predicts the effect of theta burst stimulation in false belief reasoning:ventrolateral prefrontal cortex in false belief reasoning

When required to represent a perspective that conflicts with one's own, functional magnetic resonance imaging (fMRI) suggests that the right ventrolateral prefrontal cortex (rvlPFC) supports the inhibition of that conflicting self-perspective. The present task dissociated inhibition of self-perspective from other executive control processes by contrasting belief reasoning-a cognitive state where the presence of conflicting perspectives was manipulated-with a conative desire state wherein no systematic conflict existed. Linear modeling was used to examine the effect of continuous theta burst stimulation (cTBS) to rvlPFC on participants' reaction times in belief and desire reasoning. It was anticipated that cTBS applied to rvlPFC would affect belief but not desire reasoning, by modulating activity in the Ventral Attention System (VAS). We further anticipated that this effect would be mediated by functional connectivity within this network, which was identified using resting state fMRI and an unbiased model-free approach. Simple reaction-time analysis failed to detect an effect of cTBS. However, by additionally modeling individual measures from within the stimulated network, the hypothesized effect of cTBS to belief (but, importantly, not desire) reasoning was demonstrated. Structural morphology within the stimulated region, rvlPFC, and right temporoparietal junction were demonstrated to underlie this effect. These data provide evidence that inconsistencies found with cTBS can be mediated by the composition of the functional network that is being stimulated. We suggest that the common claim that this network constitutes the VAS explains the effect of cTBS to this network on false belief reasoning. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Network approaches for the analysis of resting state fMRI data

Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”, provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila serie temporali per ogni individuo, da più di 100 valori ciascuna. I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati, così come ogni passo della costruzione del network, richiede particolare attenzione. Per ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato tramite software appositi, e anche con nuovi metodi da noi implementati. Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione delle proprietà del network, in particolare delle misure di centralità, dal momento che pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori di centralità significativi, quando confrontati con un modello nullo. Questo comportamento `e stato investigato anche a istanti di tempo diversi, usando un approccio sliding window, applicando un test statistico basato su un modello nullo pi`u complesso. Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani per mezzo della teoria dei network.

Estabilidad del espectro de potencia en resting state: un estudio de fiabilidad con Magnetoencefalografía

La sincronización de las oscilaciones cerebrales se produce incluso en ausencia de tarea, por eso, el resting state está aportando interesantes vías de estudio de los procesos normales y patológicos. Dada la creciente necesidad por utilizar las medidas derivadas de las señales MEG en resting state como biomarcadores clínicos o en la evaluación de tratamientos, es necesario garantizar su fiabilidad. En este estudio se ha investigado por primera vez la fiabilidad de la las medidas espectrales derivadas de registros MEG explorando la estabilidad en resting state de la potencia de 10 sujetos sanos en tres sesiones con un intervalo test-retest de 7 días. A partir de las señales MEG de cada sujeto y sesión se calculó el espectro de potencia de 1 a 100Hz en cada sensor, y como medida de fiabilidad se utilizó el coeficiente de correlación intraclase (ICC). Para explorar cómo afecta la intensidad de la señal a la estabilidad, se registró la señal de la cámara vacía en cada sesión de registro y se calculó la relación señal/ruido (SNR). La potencia espectral en MEG es muy estable en las bandas de frecuencia α, β y θ, y menos estable en δ y γ-2. Con respecto a la distribución de la estabilidad, la señal capturada en la zona frontal del equipo MEG fue la menos estable a través de todas las bandas de frecuencia. La estabilidad mostró cierta tendencia a disminuir conforme disminuye la SNR; este efecto es parcial, ya que los ritmos cerebrales estables mostraron un alto ICC incluso con baja SNR. En conjunto, estos resultados sugieren que las medidas espectrales en resting state con MEG son suficientemente fiables para ser consideradas en futuros estudios longitudinales sobre cambios en la actividad cerebral.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Changes in Brain Resting-state Functional Connectivity Associated with Peripheral Nerve Block: A Pilot Study.

BACKGROUND: Limited information exists on the effects of temporary functional deafferentation (TFD) on brain activity after peripheral nerve block (PNB) in healthy humans. Increasingly, resting-state functional connectivity (RSFC) is being used to study brain activity and organization. The purpose of this study was to test the hypothesis that TFD through PNB will influence changes in RSFC plasticity in central sensorimotor functional brain networks in healthy human participants. METHODS: The authors achieved TFD using a supraclavicular PNB model with 10 healthy human participants undergoing functional connectivity magnetic resonance imaging before PNB, during active PNB, and during PNB recovery. RSFC differences among study conditions were determined by multiple-comparison-corrected (false discovery rate-corrected P value less than 0.05) random-effects, between-condition, and seed-to-voxel analyses using the left and right manual motor regions. RESULTS: The results of this pilot study demonstrated disruption of interhemispheric left-to-right manual motor region RSFC (e.g., mean Fisher-transformed z [effect size] at pre-PNB 1.05 vs. 0.55 during PNB) but preservation of intrahemispheric RSFC of these regions during PNB. Additionally, there was increased RSFC between the left motor region of interest (PNB-affected area) and bilateral higher order visual cortex regions after clinical PNB resolution (e.g., Fisher z between left motor region of interest and right and left lingual gyrus regions during PNB, -0.1 and -0.6 vs. 0.22 and 0.18 after PNB resolution, respectively). CONCLUSIONS: This pilot study provides evidence that PNB has features consistent with other models of deafferentation, making it a potentially useful approach to investigate brain plasticity. The findings provide insight into RSFC of sensorimotor functional brain networks during PNB and PNB recovery and support modulation of the sensory-motor integration feedback loop as a mechanism for explaining the behavioral correlates of peripherally induced TFD through PNB.

Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space

Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.

Feasibility of resting-state microstates neurofeedback

Despite major progress, currently available treatment options for patients suffering from schizophrenia remain suboptimal. Antipsychotic medication is one such option, and is helpful in acute phases of the disease. However, antipsychotics cause significant side-effects that often require additional medication, and can even trigger the discontinuation of treatment. Taken together, along with the fact that 20-30% of patients are medication-resistant, it is clear that new medical care options should be developed for patients with schizophrenia. Besides medication, an emerging option to treat psychiatric symptoms is through the use of neurofeedback. This technique has proven efficacy for other disorders and, more importantly, has also proven to be feasible in patients with schizophrenia. One of the major advantages of this approach is that it allows for the influence of brain states that otherwise would be inaccessible; i.e. the physiological markers underlying psychotic symptoms. EEG resting-state microstates are a very interesting electrophysiological marker of schizophrenia symptoms. Precisely, a specific class of resting-state microstates, namely microstate class D, has consistently been found to show a temporal shortening in patients with schizophrenia compared to controls, and this shortening is correlated with the presence positive psychotic symptoms. Under the scope of biological psychiatry, appropriate treatment of psychotic symptoms can be expected to modify the underlying physiological markers accompanying behavioral manifestations of a disease. We reason that if abnormal temporal parameters of resting-state microstates seem to be related to positive symptoms in schizophrenia, regulating this EEG feature might be helpful as a treatment for patients. The goal of this thesis was to prove the feasibility of microstate class D contribution self-regulation via neurofeedback. Given that no other study has attempted to regulate microstates via neurofeedback, we first tested its feasibility in a population of healthy subjects. In the first paper we describe the methodological characteristics of the neurofeedback protocol and its implementation. Neurofeedback performance was assessed by means of linear mixed effects modeling, which provided a complete profile of the neurofeedback’s training response within and between-subjects. The protocol included 20 training sessions, and each session contained three conditions: baseline (resting-state) and two active conditions: training (auditory feedback upon self-regulation performance) and transfer (self-regulation with no feedback). With linear modeling we obtained performance indices for each of them as follows: baseline carryover (baseline increments time-dependent) and learning and aptitude for each of the active conditions. Learning refers to the increase/decrease of the microstate class D contribution, time-dependent during each active condition, and aptitude refers to the constant difference of the microstate class D contribution between each active condition and baseline independent of time. The indices provided are discussed in terms of tailoring neurofeedback treatment to individual profiles so that it can be applied in future studies or clinical practice. In our sample of participants, neurofeedback proved feasible, as all participants at least showed positive results in one of the aforementioned learning indices. Furthermore, between-subjects we observed that the contribution of microstate class D across-sessions increased by 0.42% during baseline, 1.93% during training trials, and 1.83% during transfer. This range is expected to be effective in treating psychotic symptoms in patients. In the second paper presented in this thesis, we explored the possible predictors of neurofeedback success among psychological variables measured with questionnaires. An interesting finding was the negative correlation between “motivational incongruence” and some of the neurofeedback performance indices. Even though this finding requires replication, we discuss it in terms of the interfering effects of incompatible psychological processes with neurofeedback training requirements. In the third paper, we present a meta-analysis on all available studies that have related resting-state microstate abnormalities and schizophrenia. We obtained medium effect sizes for two microstate classes, namely C and D. Combining the meta-analysis results with the fact that microstate class D abnormalities are correlated with the presence of positive symptoms in patients with schizophrenia, these results add further support for the training of this precise microstate. Overall, the results obtained in this study encourage the implementation of this protocol in a population of patients with schizophrenia. However, future studies will have to show whether patients will be able to successfully self-regulate the contribution of microstate class D and, if so, whether this regulation will have an impact on symptomatology.

Meditators and non-meditators: EEG source imaging during resting

Many meditation exercises aim at increased awareness of ongoing experiences through sustained attention and at detachment, i.e., non-engaging observation of these ongoing experiences by the intent not to analyze, judge or expect anything. Long-term meditation practice is believed to generalize the ability of increased awareness and greater detachment into everyday life. We hypothesized that neuroplasticity effects of meditation (correlates of increased awareness and detachment) would be detectable in a no-task resting state. EEG recorded during resting was compared between Qigong meditators and controls. Using LORETA (low resolution electromagnetic tomography) to compute the intracerebral source locations, differences in brain activations between groups were found in the inhibitory delta EEG frequency band. In the meditators, appraisal systems were inhibited, while brain areas involved in the detection and integration of internal and external sensory information showed increased activation. This suggests that neuroplasticity effects of long-term meditation practice, subjectively described as increased awareness and greater detachment, are carried over into non-meditating states.

Functional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks

Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain's intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2×2×2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test-retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905±0.033 between-sessions; 0.885±0.052 between-scanners) than ASL (0.545±0.048; 0.575±0.059). Nevertheless, ASL provided highly reproducible (0.955±0.021; 0.970±0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.

The brain’s default state interacts with working memory processes in a complex and load-dependent manner

Recently, many studies about a network active during rest and deactivated during tasks emerged in the literature: the default mode network (DMN). Spatial and temporal DMN features are important markers for psychiatric diseases. Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects. From these findings, the conclusion can be drawn that an increase in resting state DMN activity may go along with an increase in theta power in high-load WM conditions. We followed this hypothesis in a study on 17 healthy subjects performing a visual Sternberg WM task. The DMN was obtained by a BOLD-ICA approach and its dynamics represented by the percent-strength during pre-stimulus periods. DMN dynamics were temporally correlated with EEG theta spectral power from retention intervals. This so-called covariance mapping yielded the spatial distribution of the theta EEG fluctuations associated with the dynamics of the DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention. However, load-dependent correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance during later retention. Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over the course of the retention period. Since both, WM performance and DMN activity, are markers of mental health, our results could be important for further investigations of psychiatric populations.

The brain’s pre-stimulus default state interacts with working memory in a load-dependent manner

Recently, multiple studies showed that spatial and temporal features of a task-negative default mode network (DMN) (Greicius et al., 2003) are important markers for psychiatric diseases (Balsters et al., 2013). Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG and MEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects (Brookes et al., 2011). Negative correlations between DMN activity and theta amplitude have been found during resting state (Jann et al., 2010) as well as during WM (Michels et al., 2010). Likewise, WM training resulted in higher resting state theta power as well as increased small-worldness of the resting brain (Langer et al., 2013). Further, increased fMRI connectivity between nodes of the DMN correlated with better WM performance (Hampson et al., 2006). Hence, the brain’s default state might influence it’s functioning during task. We therefore hypothesized correlations between pre-stimulus DMN activity and EEG-theta power during WM maintenance, depending on the WM load. 17 healthy subjects performed a Sternberg WM task while being measured simultaneously with EEG and fMRI. Data was recorded within a multicenter-study: 12 subjects were measured in Zurich with a 64-channels MR-compatible system (Brain Products) in a 3T Philips scanner, 5 subjects with a 96-channel MR-compatible system (Brain Products) in a 3T Siemens Scanner in Bern. The DMN components was obtained by a group BOLD-ICA approach over the full task duration (figure 1). The subject-wise dynamics were obtained by back-reconstructed onto each subject’s fMRI data and normalized to percent signal change values. The single trial pre-stimulus-DMN activation was then temporally correlated with the single trial EEG-theta (3-8 Hz) spectral power during retention intervals. This so-called covariance mapping (Jann et al., 2010) yielded the spatial distribution of the theta EEG fluctuations during retention associated with the dynamics of the pre-stimulus DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention (figure 2). However, correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance in the middle of the retention period (TANOVA, p<0.05) (figure 3). Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over time. While at a more global, load-independent view pre-stimulus DMN activity correlated positively with theta power during retention, the correlation was inversed during certain time windows in high-load trials, meaning that in trials with enhanced pre-stimulus DMN activity theta power decreases during retention. Since both WM performance and DMN activity are markers of mental health our results could be important for further investigations of psychiatric populations.

L-dopa modulates functional connectivity in striatal cognitive and motor networks: A double-blind placebo-controlled study

Functional connectivity (FC) analyses of resting-state fMRI data allow for the mapping of large-scale functional networks, and provide a novel means of examining the impact of dopaminergic challenge. Here, using a double-blind, placebo-controlled design, we examined the effect of L-dopa, a dopamine precursor, on striatal resting-state FC in 19 healthy young adults.Weexamined the FC of 6 striatal regions of interest (ROIs) previously shown to elicit networks known to be associated with motivational, cognitive and motor subdivisions of the caudate and putamen (Di Martino et al., 2008). In addition to replicating the previously demonstrated patterns of striatal FC, we observed robust effects of L-dopa. Specifically, L-dopa increased FC in motor pathways connecting the putamen ROIs with the cerebellum and brainstem. Although L-dopa also increased FC between the inferior ventral striatum and ventrolateral prefrontal cortex, it disrupted ventral striatal and dorsal caudate FC with the default mode network. These alterations in FC are consistent with studies that have demonstrated dopaminergic modulation of cognitive and motor striatal networks in healthy participants. Recent studies have demonstrated altered resting state FC in several conditions believed to be characterized by abnormal dopaminergic neurotransmission. Our findings suggest that the application of similar experimental pharmacological manipulations in such populations may further our understanding of the role of dopaminergic neurotransmission in those conditions.