Anatomy and Neuroscience - Research Publications

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    EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel.
    Babiloni, C ; Arakaki, X ; Bonanni, L ; Bujan, A ; Carrillo, MC ; Del Percio, C ; Edelmayer, RM ; Egan, G ; Elahh, FM ; Evans, A ; Ferri, R ; Frisoni, GB ; Güntekin, B ; Hainsworth, A ; Hampel, H ; Jelic, V ; Jeong, J ; Kim, DK ; Kramberger, M ; Kumar, S ; Lizio, R ; Nobili, F ; Noce, G ; Puce, A ; Ritter, P ; Smit, DJA ; Soricelli, A ; Teipel, S ; Tucci, F ; Sachdev, P ; Valdes-Sosa, M ; Valdes-Sosa, P ; Vergallo, A ; Yener, G (Elsevier BV, 2021-07)
    Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.
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    Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method
    Li, S ; Jamadar, SD ; Ward, PGD ; Egan, GF ; Chen, Z (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-02-01)
    Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.
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    Dissociating neural variability related to stimulus quality and response times in perceptual decision-making
    Bode, S ; Bennett, D ; Sewell, DK ; Paton, B ; Egan, GF ; Smith, PL ; Murawski, C (Elsevier, 2018-03-01)
    According to sequential sampling models, perceptual decision-making is based on accumulation of noisy evidence towards a decision threshold. The speed with which a decision is reached is determined by both the quality of incoming sensory information and random trial-by-trial variability in the encoded stimulus representations. To investigate those decision dynamics at the neural level, participants made perceptual decisions while functional magnetic resonance imaging (fMRI) was conducted. On each trial, participants judged whether an image presented under conditions of high, medium, or low visual noise showed a piano or a chair. Higher stimulus quality (lower visual noise) was associated with increased activation in bilateral medial occipito-temporal cortex and ventral striatum. Lower stimulus quality was related to stronger activation in posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). When stimulus quality was fixed, faster response times were associated with a positive parametric modulation of activation in medial prefrontal and orbitofrontal cortex, while slower response times were again related to more activation in PPC, DLPFC and insula. Our results suggest that distinct neural networks were sensitive to the quality of stimulus information, and to trial-to-trial variability in the encoded stimulus representations, but that reaching a decision was a consequence of their joint activity.
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    Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis
    Bhattarai, A ; Egan, GF ; Talman, P ; Chua, P ; Chen, Z (WILEY, 2021-02-15)
    Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.
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    Does transcranial electrical stimulation enhance corticospinal excitability of the motor cortex in healthy individuals? A systematic review and meta-analysis
    Dissanayaka, T ; Zoghi, M ; Farrell, M ; Egan, GF ; Jaberzadeh, S (WILEY, 2017-08-01)
    Numerous studies have explored the effects of transcranial electrical stimulation (tES) - including anodal transcranial direct current stimulation (a-tDCS), cathodal transcranial direct current stimulation (c-tDCS), transcranial alternative current stimulation (tACS), transcranial random noise stimulation (tRNS) and transcranial pulsed current stimulation (tPCS) - on corticospinal excitability (CSE) in healthy populations. However, the efficacy of these techniques and their optimal parameters for producing robust results has not been studied. Thus, the aim of this systematic review was to consolidate current knowledge about the effects of various parameters of a-tDCS, c-tDCS, tACS, tRNS and tPCS on the CSE of the primary motor cortex (M1) in healthy people. Leading electronic databases were searched for relevant studies published between January 1990 and February 2017; 126 articles were identified, and their results were extracted and analysed using RevMan software. The meta-analysis showed that a-tDCS application on the dominant side significantly increases CSE (P < 0.01) and that the efficacy of a-tDCS is dependent on current density and duration of application. Similar results were obtained for stimulation of M1 on the non-dominant side (P = 0.003). The effects of a-tDCS reduce significantly after 24 h (P = 0.006). Meta-analysis also revealed significant reduction in CSE following c-tDCS (P < 0.001) and significant increases after tRNS (P = 0.03) and tPCS (P = 0.01). However, tACS effects on CSE were only significant when the stimulation frequency was ≥140 Hz. This review provides evidence that tES has substantial effects on CSE in healthy individuals for a range of stimulus parameters.
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    Cerebral Compensation During Motor Function in Friedreich Ataxia: The IMAGE-FRDA Study
    Harding, IH ; Corben, LA ; Delatycki, MB ; Stagnitti, MR ; Storey, E ; Egan, GF ; Georgiou-Karistianis, N (WILEY, 2017-08-01)
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    Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation
    Pawar, K ; Chen, Z ; Shah, NJ ; Egan, GF (WILEY, 2019-12-22)
    The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.
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    Comparison of Rossini-Rothwell and adaptive threshold-hunting methods on the stability of TMS induced motor evoked potentials amplitudes
    Dissanayaka, T ; Zoghi, M ; Farrell, M ; Egan, G ; Jaberzadeh, S (WILEY, 2018-11-01)
    Several methods can be used to determine the resting motor threshold (RMT) and by that recording transcranial magnetic stimulation (TMS) induced motor evoked potentials (MEPs). However, no research has compared the test retest reliability of these methods. Thus, the aim of this study was to determine intra- and inter-session reliability of Rossini-Rothwell (R-R) and parameter estimation by sequential testing (PEST) methods on TMS-induced MEPs and comparison of these two methods on RMT. Twelve healthy individuals participated in this study three times (T1, T2 and T3) over two days. TMS was applied using both R-R and PEST to estimate RMT and average of 25 MEPs were acquired at each of the three time points. The intra-class correlation coefficient indicated high intra-session reliability in the MEP amplitudes for both methods (0.79 and 0.88, R-R and PEST respectively). The RMT and MEP amplitudes had higher inter-session reliability in both methods (0.99 and 0.998, R-R and PEST respectively; 0.84 and 0.76, R-R and PEST respectively). There was no significant difference between methods for RMT at both T1 (maximum stimulator output of R-R vs. PEST, 33.7% ± 7.7% vs. 33.8% ± 7.6%, p = 0.75) and T3 (maximum stimulator output of R-R vs. PEST, 33.5% ± 7.3% vs. 33.7% ± 7.3%, p = 0.19). There was a significant positive correlation between the methods' estimates of RMT, with PEST requiring significantly fewer stimuli. This study shows that the R-R and PEST methods have high intra-and inter-session reliability and the same precision, with PEST having the advantage over R-R in speed of estimation of RMT.
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    Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET
    Sudarshan, VP ; Li, S ; Jamadar, SD ; Egan, GF ; Awate, SP ; Chen, Z (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-03-19)
    Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.
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    Individual differences in haemoglobin concentration influence bold fMRI functional connectivity and its correlation with cognition
    Ward, PGD ; Orchard, ER ; Oldham, S ; Arnatkeviciute, A ; Sforazzini, F ; Fornito, A ; Storey, E ; Egan, GF ; Jamadar, SD (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2020-11-01)
    Resting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest. In a cohort of 518 healthy elderly subjects (259 men), each sex group was median-split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohen's d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlation values, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females. Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships.