Browsing by Author "Moran, Rosalyn J."

Now showing 1 - 13 of 13
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    Aging into Perceptual Control: A Dynamic Causal Modeling for fMRI Study of Bistable Perception
    Dowlati, Ehsan; Adams, Sarah E.; Stiles, Alexandra; Moran, Rosalyn J. (Frontiers, 2016-03-31)
    Aging is accompanied by stereotyped changes in functional brain activations, for example a cortical shift in activity patterns from posterior to anterior regions is one hallmark revealed by functional magnetic resonance imaging (fMRI) of aging cognition. Whether these neuronal effects of aging could potentially contribute to an amelioration of or resistance to the cognitive symptoms associated with psychopathology remains to be explored. We used a visual illusion paradigm to address whether aging affects the cortical control of perceptual beliefs and biases. Our aim was to understand the effective connectivity associated with volitional control of ambiguous visual stimuli and to test whether greater top-down control of early visual networks emerged with advancing age. Using a bias training paradigm for ambiguous images we found that older participants (n = 16) resisted experimenter-induced visual bias compared to a younger cohort (n = 14) and that this resistance was associated with greater activity in prefrontal and temporal cortices. By applying Dynamic Causal Models for fMRI we uncovered a selective recruitment of top-down connections from the middle temporal to Lingual gyrus (LIN) by the older cohort during the perceptual switch decision following bias training. In contrast, our younger cohort did not exhibit any consistent connectivity effects but instead showed a loss of driving inputs to orbitofrontal sources following training. These findings suggest that perceptual beliefs are more readily controlled by top-down strategies in older adults and introduce age-dependent neural mechanisms that may be important for understanding aberrant belief states associated with psychopathology.
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    Alterations in Brain Connectivity Underlying Beta Oscillations in Parkinsonism
    Moran, Rosalyn J.; Mallet, Nicolas; Litvak, Vladimir; Dolan, Raymond J.; Magill, Peter J.; Friston, Karl J.; Brown, Peter (PLOS, 2011-08-11)
    Cortico-basal ganglia-thalamocortical circuits are severely disrupted by the dopamine depletion of Parkinson’s disease (PD), leading to pathologically exaggerated beta oscillations. Abnormal rhythms, found in several circuit nodes are correlated with movement impairments but their neural basis remains unclear. Here, we used dynamic causal modeling (DCM) and the 6-hydroxydopamine-lesioned rat model of PD to examine the effective connectivity underlying these spectral abnormalities. We acquired auto-spectral and cross-spectral measures of beta oscillations (10–35 Hz) from local field potential recordings made simultaneously in the frontal cortex, striatum, external globus pallidus (GPe) and subthalamic nucleus (STN), and used these data to optimise neurobiologically plausible models. Chronic dopamine depletion reorganised the cortico-basal ganglia-thalamocortical circuit, with increased effective connectivity in the pathway from cortex to STN and decreased connectivity from STN to GPe. Moreover, a contribution analysis of the Parkinsonian circuit distinguished between pathogenic and compensatory processes and revealed how effective connectivity along the indirect pathway acquired a strategic importance that underpins beta oscillations. In modeling excessive beta synchrony in PD, these findings provide a novel perspective on how altered connectivity in basal ganglia-thalamocortical circuits reflects a balance between pathogenesis and compensation, and predicts potential new therapeutic targets to overcome dysfunctional oscillations.
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    The chronometry of risk processing in the human cortex
    Symmonds, Mkael; Moran, Rosalyn J.; Wright, Nicholas D.; Bossaerts, Peter; Barnes, Gareth; Dolan, Raymond J. (Frontiers, 2013-08-20)
    The neuroscience of human decision-making has focused on localizing brain activity correlating with decision variables and choice, most commonly using functional MRI (fMRI). Poor temporal resolution means these studies are agnostic in relation to how decisions unfold in time. Consequently, here we address the temporal evolution of neural activity related to encoding of risk using magnetoencephalography (MEG), and show modulations of electromagnetic power in posterior parietal and dorsomedial prefrontal cortex (DMPFC) which scale with both variance and skewness in a lottery, detectable within 500 ms following stimulus presentation. Electromagnetic responses in somatosensory cortex following this risk encoding predict subsequent choices. Furthermore, within anterior insula we observed early and late effects of subject-specific risk preferences, suggestive of a role in both risk assessment and risk anticipation during choice. The observation that cortical activity tracks specific and independent components of risk from early time-points in a decision-making task supports the hypothesis that specialized brain circuitry underpins risk perception.
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    Circadian dynamics in measures of cortical excitation and inhibition balance
    Chellappa, Sarah L.; Gaggioni, Giulia; Ly, Julien Q. M.; Papachilleos, Soterios; Borsu, Chloe; Brzozowski, Alexandre; Rosanova, Mario; Sarasso, Simone; Luxen, Andre; Middleton, Benita; Archer, Simon N.; Dijk, Derk-Jan; Massimini, Marcello; Maquet, Pierre; Phillips, Christophe; Moran, Rosalyn J.; Vandewalle, Gilles (Springer Nature, 2016-09-21)
    Several neuropsychiatric and neurological disorders have recently been characterized as dysfunctions arising from a 'final common pathway' of imbalanced excitation to inhibition within cortical networks. How the regulation of a cortical E/I ratio is affected by sleep and the circadian rhythm however, remains to be established. Here we addressed this issue through the analyses of TMS-evoked responses recorded over a 29 h sleep deprivation protocol conducted in young and healthy volunteers. Spectral analyses of TMS-evoked responses in frontal cortex revealed non-linear changes in gamma band evoked oscillations, compatible with an influence of circadian timing on inhibitory interneuron activity. In silico inferences of cell-to-cell excitatory and inhibitory connectivity and GABA/Glutamate receptor time constant based on neural mass modeling within the Dynamic causal modeling framework, further suggested excitation/inhibition balance was under a strong circadian influence. These results indicate that circadian changes in EEG spectral properties, in measure of excitatory/inhibitory connectivity and in GABA/glutamate receptor function could support the maintenance of cognitive performance during a normal waking day, but also during overnight wakefulness. More generally, these findings demonstrate a slow daily regulation of cortical excitation/inhibition balance, which depends on circadian-timing and prior sleep-wake history.
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    Dynamic Causal Models and Physiological Inference: A Validation Study Using Isoflurane Anaesthesia in Rodents
    Moran, Rosalyn J.; Jung, Fabienne; Kumagai, Tetsuya; Endepols, Heike; Graf, Rudolf; Dolan, Raymond J.; Friston, Karl J.; Stephan, Klaas Enno; Tittgemeyer, Marc (PLOS, 2011)
    Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABAA receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.
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    EEG-Based Control of Working Memory Maintenance Using Closed-Loop Binaural Stimulation
    Beauchene, Christine Elizabeth (Virginia Tech, 2018-05-17)
    The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency which can be used to modulate behavioral performance and cortical connectivity. Currently, all binaural stimulation therapeutic systems are open-loop "one-size-fits-all" approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI), which causes cognitive impairments beyond those expected based on age. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, I will present a comparison of algorithms to determine brain connectivity, results of open-loop based binaural stimulation, the development of a closed-loop brain network simulation platform, and finally an experimental study to determine the effectiveness of closed-loop control to modulate brain networks hence influencing cognitive abilities.
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    The Effect of Binaural Beats on Visuospatial Working Memory and Cortical Connectivity
    Beauchene, Christine; Abaid, Nicole; Moran, Rosalyn J.; Diana, Rachel A.; Leonessa, Alexander (PLOS, 2016-11-28)
    Binaural beats utilize a phenomenon that occurs within the cortex when two different frequencies are presented separately to each ear. This procedure produces a third phantom binaural beat, whose frequency is equal to the difference of the two presented tones and which can be manipulated for non-invasive brain stimulation. The effects of binaural beats on working memory, the system in control of temporary retention and online organization of thoughts for successful goal directed behavior, have not been well studied. Furthermore, no studies have evaluated the effects of binaural beats on brain connectivity during working memory tasks. In this study, we determined the effects of different acoustic stimulation conditions on participant response accuracy and cortical network topology, as measured by EEG recordings, during a visuospatial working memory task. Three acoustic stimulation control conditions and three binaural beat stimulation conditions were used: None, Pure Tone, Classical Music, 5Hz binaural beats, 10Hz binaural beats, and 15Hz binaural beats. We found that listening to 15Hz binaural beats during a visuospatial working memory task not only increased the response accuracy, but also modified the strengths of the cortical networks during the task. The three auditory control conditions and the 5Hz and 10Hz binaural beats all decreased accuracy. Based on graphical network analyses, the cortical activity during 15Hz binaural beats produced networks characteristic of high information transfer with consistent connection strengths throughout the visuospatial working memory task.
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    Improving Fast-Scan Cyclic Voltammetry and Raman Spectroscopy Measurements of Dopamine and Serotonin Concentrations via the Elastic Net
    Long, Hunter Wayne (Virginia Tech, 2016-06-30)
    Dopamine and serotonin are two neurotransmitters known to both play a very important role in the human brain. For example, the death of dopamine producing neurons in a region of the brain known as the substantia nigra are known to cause the motor symptoms of Parkinson's disease. Also, many antidepressants are believed to work by increasing the extracellular level of serotonin in the brain. For the first time, it is now possible to measure the release of these two chemicals at sub-second time resolution in a human brain using a technique known as fast-scan cyclic voltammetry, for example from patients undergoing deep brain stimulation (DBS) electrode implantation surgery. In this work, we aimed to assess the feasibility of obtaining veridical dual measurements of serotonin and dopamine from substrates with mixtures of both chemicals. In the wet lab, data was collected on known concentrations of dopamine and serotonin and then used to make models capable of estimating the concentration of both chemicals from the voltammograms recorded in the patients. A method of linear regression known as the elastic net was used to make models from the wet lab data. The wetlab data was used to compare the performance of univariate and multivariate type models over various concentration ranges from 0-8000nM of dopamine and serotonin. Cross validation revealed that the multivariate model outperformed the univariate model both in terms of the linear correlation between predictions and actual values, and pH induced noise. The pH induced noise for the univariate model was 3.4 times greater for dopamine and 4.1 times greater for serotonin than the multivariate model. Raman spectroscopy was also investigated as a possible alternative to fast-scan cyclic voltammetry. Raman spectroscopy could have several benefits over fast-scan cyclic voltammetry, including the ability to chronically implant the measurement probe into a patient's brain and make observations over a long period of time. Raman spectroscopy data was collected on known concentrations of dopamine to investigate its potential in making in vivo measurements, however this data collection failed. Therefore, simulations were made which revealed the potential of the elastic net algorithm to determine the Raman spectra of several neurotransmitters simultaneously, even when they are in mixtures and the spectra are obstructed by the noisy background. The multivariate type model outperformed the univariate type model on Raman spectroscopy data and was able to predict dopamine with an error of 805nM RMS and serotonin with an error of 475nM RMS after being trained on concentrations smaller than 5uM of both dopamine and serotonin. In addition, the original Raman spectra of both neurotransmitters was extracted from the noise and reproduced very accurately by this method.
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    Inputs to prefrontal cortex support visual recognition in the aging brain
    Gilbert, Jessica R.; Moran, Rosalyn J. (Scientific Reports, 2016-08-23)
    Predictive coding models of brain function propose that top-down cortical signals promote efficient neural codes by carrying predictions of upcoming sensory events. We hypothesized that older brains would employ these codes more prominently given their longer repertoire of sensory experience. We measured the connectivity underlying stimulus-evoked responses in cortical visual networks using electroencephalography and dynamic causal modeling and found that in young adults with reported normal or corrected-to-normal vision, signals propagated from early visual regions and reverberated along reciprocal connections to temporal, parietal and frontal cortices, while in contrast, the network was driven by both early visual and prefrontal inputs in older adults with reported normal or corrected-to-normal vision. Previously thought of as exceptions to the rule of bottom-up signal propagation, our results demonstrate a prominent role for prefrontal inputs in driving vision in aged brains in line with lifespan-dependent predictive neural codes.
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    Mismatch Responses in the Awake Rat: Evidence from Epidural Recordings of Auditory Cortical Fields
    Jung, Fabienne; Stephan, Klaas Enno; Backes, Heiko; Moran, Rosalyn J.; Gramer, Markus; Kumagai, Tetsuya; Graf, Rudolf; Endepols, Heike; Tittgemeyer, Marc (PLOS, 2013-04-30)
    Detecting sudden environmental changes is crucial for the survival of humans and animals. In the human auditory system the mismatch negativity (MMN), a component of auditory evoked potentials (AEPs), reflects the violation of predictable stimulus regularities, established by the previous auditory sequence. Given the considerable potentiality of the MMN for clinical applications, establishing valid animal models that allow for detailed investigation of its neurophysiological mechanisms is important. Rodent studies, so far almost exclusively under anesthesia, have not provided decisive evidence whether an MMN analogue exists in rats. This may be due to several factors, including the effect of anesthesia. We therefore used epidural recordings in awake black hooded rats, from two auditory cortical areas in both hemispheres, and with bandpass filtered noise stimuli that were optimized in frequency and duration for eliciting MMN in rats. Using a classical oddball paradigm with frequency deviants, we detected mismatch responses at all four electrodes in primary and secondary auditory cortex, with morphological and functional properties similar to those known in humans, i.e., large amplitude biphasic differences that increased in amplitude with decreasing deviant probability. These mismatch responses significantly diminished in a control condition that removed the predictive context while controlling for presentation rate of the deviants. While our present study does not allow for disambiguating precisely the relative contribution of adaptation and prediction error processing to the observed mismatch responses, it demonstrates that MMN-like potentials can be obtained in awake and unrestrained rats.
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    Neural masses and fields in dynamic causal modeling
    Moran, Rosalyn J.; Pinotsis, Dimitris A.; Friston, Karl J. (Frontiers, 2013-05-28)
    Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. Using an identical neuronal architecture, we show that a set of conductance based models—that consider the dynamics of specific ion-channels—present a richer space of responses; owing to non-linear interactions between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivity—through local lateral interactions among cortical layers—may be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structure–function relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class.
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    Peak frequency in the theta and alpha bands correlates with human working memory capacity
    Moran, Rosalyn J.; Campo, Pablo; Maestu, Fernando; Reilly, Richard B.; Dolan, Raymond J.; Strangle, Bryan A. (Frontiers, 2010-11-11)
    Theta oscillations in the local field potential of neural ensembles are considered key mediators of human working memory. Theoretical accounts arising from animal hippocampal recordings propose that the phase of theta oscillations serves to instantiate sequential neuronal firing to form discrete representations of items held online. Human evidence of phase relationships in visual working memory has enhanced this theory, implicating long theta cycles in supporting greater memory capacity. Here we use human magnetoencephalographic recordings to examine a novel, alternative principle of theta functionality. The principle we hypothesize is derived from information theory and predicts that rather than long (low frequency) theta cycles, short (high frequency) theta cycles are best suited to support high information capacity. From oscillatory activity recorded during the maintenance period of a visual working memory task we show that a network of brain regions displays an increase in peak 4–12 Hz frequency with increasing memory load. Source localization techniques reveal that this network comprises bilateral prefrontal and right parietal cortices. Further, the peak of oscillation along this theta–alpha frequency axis is significantly higher in high capacity individuals compared to low capacity individuals. Importantly while we observe the adherence of cortical neuronal oscillations to our novel principle of theta functioning, we also observe the traditional inverse effect of low frequency theta maintaining high loads, where critically this was located in medial temporal regions suggesting parallel, dissociable hippocampal-centric, and prefrontal-centric theta mechanisms.
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    Understanding social function in psychiatric illnesses through computational modeling and multiplayer games
    Cui, Zhuoya (Virginia Tech, 2021-05-26)
    Impaired social functioning conferred by mental illnesses has been constantly implicated in previous literatures. However, studies of social abnormalities in psychiatric conditions are often challenged by the difficulties of formalizing dynamic social exchanges and quantifying their neurocognitive underpinnings. Recently, the rapid growth of computational psychiatry as a new field along with the development of multiplayer economic paradigms provide powerful tools to parameterize complex interpersonal processes and identify quantitative indicators of social impairments. By utilizing these methodologies, the current set of studies aimed to examine social decision making during multiplayer economic games in participants diagnosed with depression (study 1) and combat-related post-traumatic stress disorder (PTSD, study 2), as well as an online population with elevated symptoms of borderline personality disorder (BPD, study 3). We then quantified and disentangled the impacts of multiple latent decision-making components, mainly social valuation and social learning, on maladaptive social behavior via explanatory modeling. Different underlying alterations were revealed across diagnoses. Atypical social exchange in depression and BPD were found attributed to altered social valuation and social learning respectively, whereas both social valuation and social learning contributed to interpersonal dysfunction in PTSD. Additionally, model-derived indices of social abnormalities positively correlated with levels of symptom severity (study 1 and 2) and exhibited a longitudinal association with symptom change (study 1). Our findings provided mechanistic insights into interpersonal difficulties in psychiatric illnesses, and highlighted the importance of a computational understanding of social function which holds potential clinical implications in differential diagnosis and precise treatment.