Biology subjects

Skouras, S.

Publications and source records attributed to Skouras, S..

7 recordsLinked to original sources

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2019

Distinct effects of amyloid and tau deposition on eigenvector centrality during hippocampal down-regulation: a real-time fMRI virtual reality closed-loop neurofeedback study with CSF biomarkers

Hippocampal down-regulation is associated with genetic predisposition to Alzheimers disease (AD), neurodevelopmental processes and disease symptoms. Resting state eigenvector centrality (EC) patterns resemble those of FDG-PET in AD, they can predict self-regulation performance and they are related to functional compensation across the pathophysiological continuum of AD. We acquired cerebrospinal fluid (CSF) biomarkers from a cognitively unimpaired sample at risk for AD (N=48), to investigate the effect of {beta}- amyloid peptide 42 (A{beta}42) and phosphorylated tau (p-Tau) levels on EC during the down-regulation of hippocampal subfield cornu ammonis 1, with real-time fMRI closed-loop neurofeedback. Controlling the effects of confounding variables (age, sex, number of APOE {varepsilon}4 alleles, cognitive reserve, brain reserve and hippocampal down-regulation performance), CSF A{beta}42 levels correlated positively with EC in the anterior cingulate cortex (BA24, BA32) and primary motor cortex (BA4). CSF p-Tau levels correlated with EC positively in the ACC (BA32, BA10) ventral striatum (caudate, nucleus accumbens, putamen) and left primary somatosensory cortex (BA2), as well as negatively in the posterior cingulate cortex, precuneus, cuneus and left frontal pole (BA9). Controlling for CSF biomarkers and other prognosis variables, age correlated negatively with EC in the midcingulate cortex, insula, primary somatosensory cortex (BA2) and inferior parietal lobule (BA40), as well as positively with EC in the inferior temporal gyri. Taken together, we identified patterns of functional connectomics in individuals at risk of AD during hippocampal down-regulation, which resemble those found during resting state at advanced AD stages. Moreover, we provide a standard paradigm to replicate and extend this work on a global level. This opens new avenues for further research applications, which quantify and monitor disease progression, by identifying early alterations in the self-regulation of brain function, with potential for non-invasive prognostic screening.\n\nHighlightsO_LIACC centrality decreases with early A{beta}42\nC_LIO_LIACC centrality increases with p-Tau\nC_LIO_LIPCC centrality decreases with p-Tau\nC_LIO_LIMCC centrality decreases in healthy aging\nC_LI

neuroscience↗

bioRxiv2019

The effect of APOE genotype and streamline density volume, on hippocampal CA1 down-regulation: a real-time fMRI virtual reality neurofeedback study

Hippocampal hyperactivity is a precursor of Alzheimers disease and more prominent in APOE-{varepsilon}4 carriers. It is therefore important to investigate the processes of hippocampal self-regulation, to monitor therapeutic efficacy of preclinical interventions. We have developed a closed-loop, virtual reality neurofeedback paradigm for real-time fMRI, that provides a standardized method for quantifying processes of hippocampal self-regulation. We acquired multi-modal neuroimaging data from a sample of 53 cognitively unimpaired subjects at risk for AD and applied standard methods of structural and functional connectomics. The analyses reveal significant negative associations between hippocampal CA1 down-regulation performance and APOE-{varepsilon}4 alleles, as well as hippocampal streamline density volume. Better memory performance was associated with increased, bilateral hippocampal functional connectivity during the neurofeedback task. These are the first results to link neurofeedback performance to a genetic risk factor and structural connectivity. Further, these are the first evidence that functional cohesion between the hippocampi can reflect subtle differences in memory function, in cognitively unimpaired individuals at risk for AD. We provide a novel method to assess hippocampal function in preclinical AD, and propose it can be used to derive proxies for neural reserve.\n\nHighlightsO_LIAPOE-{varepsilon}4 alleles impact hippocampal down-regulation neurofeedback performance.\nC_LIO_LIHippocampal streamline density volume is associated with decreased hippocampal down-regulation performance.\nC_LIO_LIBilaterally cohesive hippocampal activity is associated with better memory performance.\nC_LIO_LIWe provide a novel paradigm to investigate self-regulation and brain function.\nC_LI

neuroscience↗

bioRxiv2018

Multipurpose virtual reality environment for biomedical and health applications

Virtual reality is a trending, widely accessible, contemporary technology of increasing utility to biomedical and health applications. However, most implementations of VR environments are tailored to specific applications. We describe the complete development of a novel, open-source virtual reality environment that is suitable for multipurpose biomedical and healthcare applications. The developed environment simulates an immersive (first-person perspective) run in the countryside, in a virtual landscape with various salient features. The utility of the developed VR environment has been validated via two test usage cases: an application in the context of motor rehabilitation following injury of the lower limbs and an application in the context of real-time functional magnetic resonance imaging neurofeedback, to regulate brain function in specific regions of interest. The resulting test applications suggest that the implemented approach is robust, versatile and efficient. Both applications are publicly available via a GitHub repository, in support of the Open Science initiative. We anticipate our contribution to catalyze further progress and replicability with regards to the usage of virtual reality in biomedical and health applications.\n\nIndex Terms-- Motor rehabilitation, neurofeedback, virtual reality.

bioengineering↗

bioRxiv2018

The effects of psychiatric history and age on self-regulation of the default mode network

Real-time neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and down-regulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on neurofeedback performance remain largely unknown. Using open-access analyses and real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N=136), we first modeled neurofeedback performance and learning in a group of subjects with psychiatric history (na=74) and a healthy control group (nb=62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and neurofeedback performance in each group and differences in neurofeedback performance between the two groups. Results show that in an initial session of default mode network neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. Moreover, age correlates negatively with default mode network neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no up-regulation learning in controls.

neuroscience↗

bioRxiv2018

Mechanisms of functional compensation, delineated by eigenvector centrality mapping, across the pathophysiological continuum of Alzheimer’s disease

BackgroundMechanisms of functional compensation throughout the progression of Alzheimers disease (AD) remain largely underspecified. By investigating functional connectomics in relation to cerebrospinal fluid (CSF) biomarkers across the pathophysiological continuum of AD, we identify disease-stage-specific patterns of functional degradation and functional compensation.\n\nMethodsData from a sample of 96 participants, comprised of 49 controls, 11 preclinical AD subjects, 21 patients with mild cognitive impairment (MCI) due to AD and 15 patients with mild dementia due to AD, were analyzed. CSF ratio of phosphorylated tau protein over amyloid beta peptide 42 (p-tau/A{beta}42) was computed and used as a marker of progression along the AD continuum. Whole-brain, voxel-wise eigenvector centrality mapping (ECM) was computed from resting-state fMRI and regression against p-tau/A{beta}42 was performed. Surviving clusters were used as data-derived seeds in functional connectivity analyses and investigated in relation to memory performance scores (delayed free recall and memory alteration) via complementary regression models. To investigate disease-stage-specific effects, the whole-brain connectivity maps of each cluster were compared between progressive groups.\n\nResultsDecreasing centrality in the inferior parietal lobule (IPL) is significantly correlated with the p-tau/A{beta}42 ratio and associated to memory function impairment across the AD continuum. The thalamus, anterior cingulate (ACC), midcingulate (MCC) and posterior cingulate cortex (PCC) show the opposite effect. The MCC shows the highest increase in centrality as memory performance decays. In the asymptomatic preclinical group, MCC shows reduced functional connectivity (FC) with the left hippocampus and stronger FC with the precuneus (PCu). Additionally, IPL shows reduced FC with the cerebellum, compensated by stronger FC between cerebellum and PCC. In the MCI group, PCC shows reduced FC with PCu, compensated by stronger FC with the left pars orbitalis, insula and temporal pole, as well as by stronger FC of MCC with its anterior and ventral neighboring areas and the cerebellum. In the mild dementia group, extensive functional decoupling occurs across the entire autobiographical memory network and functional resilience ensues in posterior regions and the cerebellum.\n\nConclusionsFunctional decoupling in preclinical AD occurs predominantly in AD-vulnerable regions (e.g. hippocampus, cerebellar lobule VI / Crus I, visual cortex, frontal pole) and coupling between MCC and PCu, as well as between PCC and cerebellum, emerge as intrinsic mechanisms of functional compensation. At the MCI stage, the PCu can no longer compensate for hippocampal decoupling, but the compensatory role of the MCC and PCC ensue into the stage of dementia. These findings shed light on the neural mechanisms of functional compensation across the pathophysiological continuum of AD, highlighting the compensatory roles of several key brain areas.\n\nHighlights- Inferior parietal lobule centrality implicated in Alzheimers disease\n- Increasing centrality in cingulate and thalamus involved in functional compensation\n- Preclinical functional alterations of hippocampus compensated by precuneus\n- Cerebellar involvement in functional compensation\n\n\nAbbreviations

neuroscience↗