Image synthesis of interictal SPECT from MRI and PET using machine learning.

Background: Cross-modality image estimation can be performed using generative adversarial networks (GANs). To date, SPECT image estimation from another medical imaging modality using this technique has not been considered. We evaluate the estimation of SPECT from MRI and PET, and additionally assess the necessity for cross-modality image registration for GAN training. Methods: We estimated interictal SPECT from PET and MRI as a single-channel input, and as a multi-channel input to the GAN. We collected data from 48 individuals with epilepsy and converted them to 3D isotropic images for consistence across the modalities. Training and testing data were prepared in native and template spaces. The Pix2pix framework within the GAN network was adopted. We evaluated the addition of the structural similarity index metric to the loss function in the GAN implementation. Root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess how well SPECT images were able to be synthesised. Results: High quality SPECT images could be synthesised in each case. On average, the use of native space images resulted in a 5.4% percentage improvement in SSIM than the use of images registered to template space. The addition of structural similarity index metric to the GAN loss function did not result in improved synthetic SPECT images. Using PET in either the single channel or dual channel implementation led to the best results, however MRI could produce SPECT images close in quality. Conclusion: Synthesis of SPECT from MRI or PET can potentially reduce the number of scans needed for epilepsy patient evaluation and reduce patient exposure to radiation.

Automated extraction of the arterial input function from brain images for parametric PET studies.

BACKGROUND: Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS: Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS: Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Introducing the Concept of Sanctuary Sites in Ischemic Stroke.

BACKGROUND: The topography of arterial territories has been defined using digital maps of supratentorial infarcts. Regions with a high probability of infarction (Pi) exist in the deep compartment due to a paucity of collaterals. However, less attention has been given to regions with a low Pi. METHODS: Using published digital maps, patients with cortical stroke and documented vessel occlusion were included. Infarcts from T2-weighted magnetic resonance images were segmented and registered onto a standard brain template (Montreal Neurological Institute 152). Segmented magnetic resonance images were averaged to yield the Pi at a voxel level. The overall Pi for the combined arterial territories was calculated to ensure that Pi was in the range of 0 to 1. Sanctuary sites were identified as regions with Pi <0.1. RESULTS: There were 154 patients (63% men; median age, 69 years; and interquartile range, 57-78 years). The magnetic resonance imaging scan used for segmentation was performed at a median interval of 35 (interquartile range, 3-66) days after stroke onset. Sanctuary sites were present in the frontal (gyrus rectus, the paracentral lobule, and orbitofrontal and precentral gyrus), parietal (postcentral, supramarginal, and angular gyrus, superior and inferior parietal lobule, and precuneus and posterior cingulate), and occipital cortex (cuneus, middle, and superior occipital gyrus). CONCLUSIONS: We propose that following vessel occlusion, there are cortical regions with a low Pi, which we termed sanctuary sites. The anatomic basis for this observation is the compensatory capacity of leptomeningeal collaterals.

A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET.

BACKGROUND: In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS: To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS: Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION: The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.

BACKGROUND: The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. RESULT: The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method. CONCLUSION: We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone.

Measures of Subjective Memory for People with Epilepsy: A Systematic Review of Measurement Properties.

People with epilepsy frequently express concern about the burden of memory problems in their everyday lives. Self-report memory questionnaires may provide valuable insight into individuals' perceptions of their everyday memory performance and changes over time. Yet, despite their potential utility, the measurement properties of self-report memory questionnaires have not been evaluated in epilepsy. This systematic review aimed to provide a critical appraisal of the measurement properties of self-report memory questionnaires for adults with epilepsy. Following protocol registration (PROSPERO CRD42020210967), a systematic search of PubMed, EMBASE, Web of Science, CINAHL, and PsychInfo from database inception until 27 May 2021 was conducted. Eligible studies were published in English-language peer-reviewed journals, recruited adults with epilepsy, and reported on the development or evaluation of the measurement properties of a self-report memory questionnaire. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology was used to evaluate each study of a measurement property, and results were qualitatively synthesised. In total, 80 articles and one test manual were located containing 153 studies of measurement properties pertinent to 23 self-report memory questionnaires. Overall, no scale could be recommended outright for the evaluation of subjective memory symptoms in adults with epilepsy. This was due to the near absence of dedicated content validation studies relevant to this population and shortcomings in the methodology and scientific reporting of available studies of structural validity. Recommendations to support the advancement and psychometric validation of self-report memory questionnaires for people with epilepsy are provided.

A custom MR-compatible dataglove for fMRI of the human motor cortex at 7T.

We present a custom-built MR-compatible data glove to capture hand motion during concurrent fMRI experiments at 7 Tesla. Thermal and phantom tests showed our data glove can be used safely and without degradation of image quality. Subject-specific Blood Oxygen Level Dependent (BOLD) signal models, for use in fMRI analysis, were constructed based on recorded kinematic measurements. Experiments revealed the relative fMRI BOLD signal contribution of flexing, extending, and sustained isotonic extension. The ability to evaluate subject performance in real-time and create subject-specific BOLD signal models enables a wide range of experimental paradigms with improved data quality.Clinical Relevance- Using an MR compatible dataglove, subject specific Blood Oxygen Signal Level Dependent (BOLD) signal models can be constructed to study how the brain implements fine motor control.

Complement Component 5 (C5) Deficiency Improves Cognitive Outcome After Traumatic Brain Injury and Enhances Treatment Effects of Complement Inhibitors C1-Inh and CR2-Crry in a Mouse Model.

A potent effector of innate immunity, the complement system contributes significantly to the pathophysiology of traumatic brain injury (TBI). This study investigated the role of the complement cascade in neurobehavioral outcomes and neuropathology after TBI. Agents acting at different levels of the complement system, including 1) C1 esterase inhibitor (C1-Inh), 2) CR2-Crry, an inhibitor of all pathways acting at C3, and 3) the selective C5aR1 antagonist, PMX205, were administered at 1 h post-TBI. Their effects were evaluated on motor function using the rotarod apparatus, cognitive function using the active place avoidance (APA) task, and brain lesion size at a chronic stage after controlled cortical impact injury in C5-sufficient (C5+/+) and C5-deficient (C5-/-) CD1 mice. In post-TBI C5+/+ mice, rotarod performance was improved by CR2-Crry, APA performance was improved by CR2-Crry and PMX205, and brain lesion size was reduced by PMX205. After TBI, C5-/- mice performed better in the APA task compared with C5+/+ mice. C5 deficiency enhanced the effect of C1-Inh on motor function and brain damage and the effect of CR2-Crry on brain damage after TBI. Our findings support critical roles for C3 in motor deficits, the C3/C5/C5aR1 axis in cognitive deficits, and C5aR1 signaling in brain damage after TBI. Findings suggest the combination of C5 inhibition with C1-Inh and CR2-Crry as potential therapeutic strategies in TBI.

Phase II randomised placebo-controlled trial of sodium selenate as a disease-modifying treatment in chronic drug-resistant temporal lobe epilepsy: the SeLECT study protocol.

INTRODUCTION: Epilepsy is one of the most common neurological conditions worldwide. Despite many antiseizure medications (ASMs) being available, up to one-third of patients do not achieve seizure control. Preclinical studies have shown treatment with sodium selenate to have a disease-modifying effect in a rat model of chronic temporal lobe epilepsy (TLE). AIM: This randomised placebo-controlled trial aims to evaluate the antiseizure and disease-modifying effects of sodium selenate in people with drug-resistant TLE. METHODS: This will be a randomised placebo-controlled trial of sodium selenate. One hundred and twenty-four adults with drug-resistant TLE and ≥4 countable seizures/month will be recruited. Outcomes of interest will be measured at baseline, week 26 and week 52 and include an 8-week seizure diary, 24-hour electroencephalogram and cognitive, neuropsychiatric and quality of life measures. Participants will then be randomised to receive a sustained release formulation of sodium selenate (initially 10 mg three times a day, increasing to 15 mg three times a day at week 4 if tolerated) or a matching placebo for 26 weeks. OUTCOMES: The primary outcome will be a consumer codesigned epilepsy-Desirability of Outcome Rank (DOOR), combining change in seizure frequency, adverse events, quality of life and ASM burden measures into a single outcome measure, compared between treatment arms over the whole 52-week period. Secondary outcomes will compare baseline measures to week 26 (antiseizure) and week 52 (disease modification). Exploratory measures will include biomarkers of treatment response. ETHICS AND DISSEMINATION: The study has been approved by the lead site, Alfred Hospital Ethics Committee (594/20). Each participant will provide written informed consent prior to any trial procedures. The results of the study will be presented at national and international conferences, published in peer-reviewed journals and disseminated through consumer organisations. CONCLUSION: This study will be the first disease-modification randomised controlled trial in patients with drug-resistant TLE. TRIAL REGISTRATION NUMBER: ANZCTR; ACTRN12623000446662.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL).

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

Effects of C1-INH Treatment on Neurobehavioral Sequelae and Late Seizures After Traumatic Brain Injury in a Mouse Model of Controlled Cortical Impact.

C1 human-derived C1 esterase inhibitor (C1-INH) is a U.S. Food and Drig Administration-approved drug with anti-inflammatory actions. In the present study, we investigated the therapeutic effects of C1-INH on acute and chronic neurobehavioral outcomes and on seizures in the chronic stage in a mouse traumatic brain injury (TBI) model. Adult male CD1 mice were subjected to controlled cortical impact and randomly allocated to receive C1-INH or vehicle solution 1 h post-TBI. Effects of C1-INH treatment on inflammatory responses and brain damage after TBI were examined using the Cytometric Bead Array, C5a enzyme-linked immunosorbent assay, Fluoro-Jade C staining, and Nissl staining. Neurobehavioral outcomes after TBI were assessed with modified neurological severity scores, the rotarod and open field tests, and the active place avoidance task. Video-electroencephalographic monitoring was performed in the 15th and 16th weeks after TBI to document epileptic seizures. We found that C1-INH treatment reduced TNFα expression and alleviated brain damage. Treatment with C1-INH improved neurological functions, increased locomotor activity, alleviated anxiety-like behavior, and exhibited an effect on seizures in the chronic stage after TBI. These findings suggest that C1-INH has beneficial effects on the treatment of TBI.

Usefulness and yield of routine electroencephalogram: a retrospective study.

BACKGROUND: The electroencephalogram (EEG) is a common diagnostic tool used to investigate patients for various indications including seizure disorders. AIMS: To investigate factors that predict the presence of epileptiform abnormalities on EEG and review the common indications for ordering an EEG. METHODS: We retrospectively reviewed all routine adult EEG performed in a hospital over a 6-month period. Data collated included patient demographics, clinical indication for EEG, setting in which EEG was performed, activation procedures utilised, history of epilepsy, and whether the patient was on antiepileptic medication. Our primary objective was to evaluate the factors that were predictive of an EEG with epileptiform abnormalities. RESULTS: Two hundred and thirty-nine routine EEG were included with indications, including first seizure (25.9%), known epilepsy (25.1%), cognitive change (15.9%), syncope (15.0%), movement disorder (6.7%), psychogenic non-epileptic events (5.4%), unresponsiveness/intensive care unit (4.6%) and psychiatric presentation (1.3%). Most (48.1%) EEG were normal; 8.9% of the EEG demonstrated epileptiform abnormalities. Using multivariate logistic regression, three variables proved significant in predicting an EEG with epileptiform abnormalities. Any seizure as an indication (first seizure or seizure in known epileptic), increasing patient age, and EEG conducted in an inpatient setting and within 48 h of seizure event were all statistically more likely to yield epileptiform abnormalities on EEG. CONCLUSIONS: Our findings suggest that careful selection of patients based on appropriate indications for EEG referral would likely improve the yield of an EEG. Depending on the indication, a normal EEG result can be of similar usefulness to an abnormal EEG demonstrating epileptiform abnormalities.

Social Cognition in Temporal and Frontal Lobe Epilepsy: Systematic Review, Meta-analysis, and Clinical Recommendations.

OBJECTIVE: Despite the importance of social cognitive functions to mental health and social adjustment, examination of these functions is absent in routine assessment of epilepsy patients. Thus, this review aims to provide a comprehensive overview of the literature on four major aspects of social cognition among temporal and frontal lobe epilepsy, which is a critical step toward designing new interventions. METHOD: Papers from 1990 to 2021 were reviewed and examined for inclusion in this study. After the deduplication process, a systematic review and meta-analysis of 44 and 40 articles, respectively, involving 113 people with frontal lobe epilepsy and 1482 people with temporal lobe epilepsy were conducted. RESULTS: Our results indicated that while patients with frontal or temporal lobe epilepsy have difficulties in all aspects of social cognition relative to nonclinical controls, the effect sizes were larger for theory of mind (g = .95), than for emotion recognition (g = .69) among temporal lobe epilepsy group. The frontal lobe epilepsy group exhibited significantly greater impairment in emotion recognition compared to temporal lobe. Additionally, people with right temporal lobe epilepsy (g =  1.10) performed more poorly than those with a left-sided (g = .90) seizure focus, specifically in the theory of mind domain. CONCLUSIONS: These data point to a potentially important difference in the severity of deficits within the emotion recognition and theory of mind abilities depending on the laterlization of seizure side. We also suggest a guide for the assessment of impairments in social cognition that can be integrated into multidisciplinary clinical evaluation for people with epilepsy.

Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals.

BACKGROUND: Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS: We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS: The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS: We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.

Spikes with and without concurrent high-frequency oscillations: Topographic relationship and neural correlates using EEG-fMRI.

OBJECTIVE: Epilepsy surgery is the best therapeutic option for patients with drug-resistant focal epilepsy. During presurgical investigation, interictal spikes can provide important information on eligibility, lateralisation and localisation of the surgical target. However, their relationship to epileptogenic tissue is variable. Interictal spikes with concurrent high-frequency oscillations (HFOs) have been postulated to reflect epileptogenic tissue more reliably. Here, we studied the voltage distribution of scalp-recorded spikes with and without concurrent HFO and identified their respective haemodynamic correlates using simultaneous electroencephalography and functional Magnetic Resonance Imaging (EEG-fMRI). METHODS: The scalp topography of spikes with and without concurrent HFOs were assessed in 31 consecutive patients with focal drug-resistant epilepsy who showed interictal spikes during presurgical evaluation. Simultaneous EEG-fMRI was then used in 17 patients with spikes and concurrent HFOs. Haemodynamic changes were obtained from the spatial correlation between the patient-specific voltage map of each spike population and the intra-scanner EEG. The haemodynamic response of spikes with and without HFOs were compared in terms of their spatial similarity, strength, the distance between activation peaks and concordance with interictal localisation. RESULTS: Twenty-five patients showed spikes with and without concurrent HFOs. Among patients with both types of spikes, most spikes were not associated with HFOs (p < 0.0001, Mann-Whitney test). Twenty of the 25 patients showed an average of 8 ± 6 (standard deviation) electrodes with significant voltage differences (p = 0.025, permutation test corrected for multiple comparisons) on scalp electrodes within and distant to the spike field. Comparing the haemodynamic response between both spike populations, we found no significant differences in the peak strength (p = 0.71, Mann-Whitney test), spatial distribution (p = 0.113, One-sample Wilcoxon test) and distance between activation peaks (p = 0.5, One-sample Wilcoxon test), with all peaks being co-localised in the same lobe. SIGNIFICANCE: Our data showed that spikes with and without HFOs have different scalp voltage distributions. However, when assessing the haemodynamic changes of each spike type, we found that both elicit similar haemodynamic changes and share high spatial similarity suggesting that the epileptic networks of spikes with and without HFOs have the same underlying neural substrate.

From CNNs to GANs for cross-modality medical image estimation.

Cross-modality image estimation involves the generation of images of one medical imaging modality from that of another modality. Convolutional neural networks (CNNs) have been shown to be useful in image-to-image intensity projections, in addition to identifying, characterising and extracting image patterns. Generative adversarial networks (GANs) use CNNs as generators and estimated images are classified as true or false based on an additional discriminator network. CNNs and GANs within the image estimation framework may be considered more generally as deep learning approaches, since medical images tend to be large in size, leading to the need for large neural networks. Most research in the CNN/GAN image estimation literature has involved the use of MRI data with the other modality primarily being PET or CT. This review provides an overview of the use of CNNs and GANs for cross-modality medical image estimation. We outline recently proposed neural networks and detail the constructs employed for CNN and GAN image-to-image synthesis. Motivations behind cross-modality image estimation are outlined as well. GANs appear to provide better utility in cross-modality image estimation in comparison with CNNs, a finding drawn based on our analysis involving metrics comparing estimated and actual images. Our final remarks highlight key challenges faced by the cross-modality medical image estimation field, including how intensity projection can be constrained by registration (unpaired versus paired data), use of image patches, additional networks, and spatially sensitive loss functions.

Neural activation during emotional interference corresponds to emotion dysregulation in stressed teachers.

Teacher stress and burnout has been associated with low job satisfaction, reduced emotional wellbeing, and poor student learning outcomes. Prolonged stress is associated with emotion dysregulation and has thus become a focus of stress interventions. This study examines emotional interference effects in a group of teachers suffering from high stress and to explore how individual differences in cognitive control, emotion dysregulation, and emotion recognition related to patterns of neural activation. Forty-nine teachers suffering moderate-high stress participated in an emotional counting Stroop task while their brain activity was imaged using functional magnetic resonance imaging. Participants viewed general or teacher specific words of either negative or neutral valence and were required to count the number of words on screen. Behavioural and neuroimaging results suggest that teachers are able to control emotional responses to negative stimuli, as no evidence of emotional interference was detected. However, patterns of neural activation revealed early shared engagement of regions involved in cognitive reappraisal during negative task conditions and unique late engagement of the hippocampus only while counting teacher-specific negative words. Further, we identified that greater emotion dysregulation was associated with increased activation of regions involved in cognitive control processes during neutral word trials. Teachers who showed slower emotion recognition performance were also found to have greater activation in regions associated with visual and word processing, specifically during the teacher specific negative word condition of the task. Future research should explore emotion regulation strategy use in teachers and utilise temporally sensitive neuroimaging techniques to further understand these findings.

Neural correlates of affective empathy in aging: A multimodal imaging and multivariate approach.

Empathy is one such social-cognitive capacity that undergoes age-related change. C urrently, however, not well understood is the structural and functional neurocircuitry underlying age-related differences in empathy. This study aimed to delineate brain structural and functional networks that subserve affective empathic response in younger and older adults using a modified version of the Multifaceted Empathy Task to both positive and negative emotions. Combining multimodal neuroimaging with multivariate partial least square analysis resulted in two novel findings in older but not younger adults: (a) faster empathic responding to negative emotions was related to greater fractional anisotropy of the anterior cingulum and greater functional activity of the anterior cingulate network; (b) however, empathic responding to positive emotions was related to greater fractional anisotropy of the posterior cingulum and greater functional activity of the posterior cingulate network. Such differentiation of structural and functional networks might have critical implications for prosocial behavior and social connections among older adults.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum.

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters ßSUB and DSUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters α and ß have shown consistent positive correlation with age in the corpus callosum, indicating α and ß are sensitive to tissue microstructural changes in ageing.

A CNN Based Software Gradiometer for Electromagnetic Background Noise Reduction in Low Field MRI Applications.

The shielding of electromagnetic noise is critical in obtaining magnetic resonance imaging measurements in the ultra-low magnetic field regime where the intrinsic signal-to-noise ratio is very small. The traditional approach of using an enclosure for electromagnetic shielding is expensive and hinders system portability. We describe here the use of a CNN-based software gradiometer to suppress the effect of electromagnetic ambient background noise sources that inductively couple into the signal detection coils. The system involves three ambient noise monitoring coils placed at a distance from the magnetic resonance signal detector. The three coils were used to synthesize the ambient noise captured by the signal detector; a convolutional neural network approach was used. Mathematical foundations are provided to justify the noise suppression framework. The results show that as much as 20-fold noise suppression can be achieved using an optimized convolutional neural network and simultaneous ambient noise measurements. The proposed approach has the potential to replace the requirement for magnetically shielded enclosures and make ultra-low field magnetic resonance imaging truly portable.