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Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data.

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35\%-50\% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms., Comment: 12 pages, 9 figures

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

People with schizophrenia exhibit reduced alpha oscillations and frontotemporal coordination of brain activity. Alpha oscillations are associated with top-down inhibition. Reduced alpha oscillations may fail to censor spurious endogenous activity, leading to auditory hallucinations. Transcranial alternating current stimulation (tACS) at the alpha frequency was shown to enhance alpha oscillations in people with schizophrenia and may thus be a network-based treatment for auditory hallucinations. We conducted a double-blind, randomized, placebo-controlled pilot clinical trial to examine the efficacy of 10-Hz tACS in treating auditory hallucinations in people with schizophrenia. 10-Hz tACS was administered in phase at the dorsolateral prefrontal cortex and the temporoparietal junction with a return current at Cz. Patients were randomized to receive tACS or sham for five consecutive days during the treatment week (40 min/day), followed by a maintenance period, during which participants received weekly tACS (40 min/visit) or sham. tACS treatment reduced general psychopathology (p p p p p p

Introduction Diagnosis of very short-segment Hirschsprung's disease (vsHD) by rectal suction biopsy is challenging as its aganglionic zone (AZ) overlaps with physiologic hypoganglionic zone and calretinin-positive mucosal nerves may extend from the transition zone (TZ) into AZ. We studied whether an increasing trend/gradient of calretinin-positive mucosal nerves along the distance from AZ toward TZ aids in diagnosis of HD. Materials and Methods In this study, 46 rectal suction biopsies from non-HD and HD, and 15 pull-through specimens from short-segment HD were evaluated by mucosal calretinin immunostain (CI) and image processing and analysis (IPA) to measure pixel count (PC, the percentage of calretinin stained pixels in the mucosa). Consecutive longitudinal sections of proximal AZ toward distal TZ in HD pull-through specimens were utilized as a vsHD surrogate model. First, we studied variability of mucosal CI in non-HD biopsies along the distance from dentate line. Second, we determined a cutoff point of mucosal CI by IPA that separated non-HD versus HD and applied this cutoff to longitudinal sections from proximal AZ to distal TZ segments in HD pull-through specimens. Third, we studied whether an increasing trend of mucosal CI was universally observed in HD pull-through. Results Our findings included a significant variability in PC along the biopsy distance in non-HD cases. Positive mucosal CI was found in proximal AZ in 6 (43%) of 14 HD pull-through, among which 1 case lacked submucosal nerve hypertrophy in the proximal AZ. All 14 HD pull-through cases showed an increasing trend/gradient of PC from AZ toward TZ. Conclusion Based on our findings, the presence or absence of mucosal CI positivity and submucosal nerve hypertrophy may not reliably diagnose vsHD in rectal suction biopsy. While we acknowledge that the density of mucosal innervation in variable contexts and anatomical locations is unknown and yet to be explored, our study suggests that an increasing trend of positive mucosal CI from AZ toward TZ by IPA might prove to be a useful tool for the diagnosis of vsHD in the future.

Both geomagnetic and solar activity fluctuate over time and have been proposed to affect human physiology. Heart rate variability (HRV) has substantial health implications regarding the ability to adapt to stressors and has been shown to be altered in many cardiovascular and neurological disorders. Intriguingly, previous work found significant, strong correlations between HRV and geomagnetic/solar activity. The purpose of this study to replicate these findings. We simultaneously measured HRV during a 30-day period. We recruited 20 healthy participants and measured their HRV for a 30-day period. We also collected geomagnetic and solar activity during this period for investigating their relationship with the HRV data. In agreement with previous work, we found several significant correlations between short-term HRV and geophysical time-series. However, after correction for autocorrelation, which is inherent in time-series, the only significant results were an increase in very low frequency during higher local geomagnetic activity and a geomagnetic anticipatory decrease in heart rate a day before the higher global geomagnetic activity. Both correlations were very low. The loss of most significant effects after this correction suggests that previous findings may be a result of autocorrelation. A further note of caution is required since our and the previous studies in the field do not correct for multiple comparisons given the exploratory analysis strategy. We thus conclude that the effects of geomagnetic and solar activity are (if present) most likely of very small effect size and we question the validity of the previous studies given the methodological concerns we have uncovered with our work.

Recently, transformer-based pretrained language models have demonstrated stellar performance in natural language understanding (NLU) tasks. For example, bidirectional encoder representations from transformers (BERT) have achieved outstanding performance through masked self-supervised pretraining and transformer-based modeling. However, the original BERT may only be effective for English-based NLU tasks, whereas its effectiveness for other languages such as Korean is limited. Thus, the applicability of BERT-based language models pretrained in languages other than English to NLU tasks based on those languages must be investigated. In this study, we comparatively evaluated seven BERT-based pretrained language models and their expected applicability to Korean NLU tasks. We used the climate technology dataset, which is a Korean-based large text classification dataset, in research proposals involving 45 classes. We found that the BERT-based model pretrained on the most recent Korean corpus performed the best in terms of Korean-based multiclass text classification. This suggests the necessity of optimal pretraining for specific NLU tasks, particularly those in languages other than English.

Application of stochastic variance reduction algorithms to iterative PET reconstruction. We investigated the SAGA and SVRG algorithms for non-TOF PET image reconstruction. Both similated data and a patient data sets were used in the analysis. We found that the stochastic algorithms can improve convergence rate and eliminate behaviour, commonly know as limit cycle behaviour, from PET reconstruction within 5 epochs.

Background: Inflammation and structural remodeling may contribute to fibrogenesis in Crohn’s disease (CD). We quantified the immunoexpression of calretinin, CD34, and calprotectin as a surrogate for mucosal innervation, telocytes (interstitial cells playing a role in networking), and inflammation, respectively, and correlated them with bowel alterations in stricturing CD.Methods: Primary resection specimens for ileal CD (n = 44, 31 stricturing CD, 13 inflammatory CD) were identified. Left-sided ulcerative colitis and trauma cases were used as controls. Proximal and distal margin and middle (diseased) sections were stained for calretinin, CD34, and calprotectin. Microscopic images were captured from the mucosa (calretinin), submucosa (calprotectin), and myenteric plexus (CD34), and the immunostaining was quantified using image processing and analysis. Bowel thickness at the corresponding sections were measured and correlated with the amount of immunoexpression.Results: A total of 2,037 images were analyzed. In stricturing CD, submucosal alteration/thickening at the stricture site correlated with calprotectin staining and inversely correlated with calretinin staining at the proximal margin. Muscularis propria alteration/thickening at the stricture site correlated with mucosal calretinin staining at the proximal margin. Submucosal alteration/thickening at the proximal margin correlated with calretinin and CD34 staining at the proximal margin and inversely correlated with CD34 staining at the stricture site. Calretinin immunostaining at the distal margin was significantly higher in stricturing CD than the controls.Conclusions: Inflammation and tissue remodeling appear to contribute to fibrogenesis in stricturing CD. Increased mucosal calretinin immunostaining distal to the diseased segment could be helpful in diagnosing CD in the right clinical context.

Julianna H Prim,1–3,* Sangtae Ahn,1,2,* Maria I Davila,1 Morgan L Alexander,1,2 Karen L McCulloch,3,4,* Flavio Fröhlich1,2,5–8,* 1Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 2Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 3Department of Allied Health Sciences, Human Movement Science Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 4Division of Physical Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 5Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 6Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 7Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 8Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA*These authors contributed equally to this workCorrespondence: Flavio FröhlichUniversity of North Carolina at Chapel Hill 116 Manning Dr, Mary Ellen Jones Building 6018, Chapel Hill, NC 27599, USATel +1 919 966 4584Email flavio_frohlich@med.unc.eduBackground: Chronic low back pain (CLBP) is characterized by an alteration in pain processing by the central nervous system that may affect autonomic nervous system (ANS) balance. Heart rate variability (HRV) reflects the balance of parasympathetic and sympathetic ANS activation. In particular, respiratory sinus arrhythmia (RSA) solely reflects parasympathetic input and is reduced in CLBP patients. Yet, it remains unknown if non-invasive brain stimulation can alter ANS balance in CLBP patients.Objective: To evaluate if non-invasive brain stimulation modulates the ANS, we analyzed HRV metrics collected in a previously published study of transcranial alternating current stimulation (tACS) for the modulation of CLBP through enhancing alpha oscillations. We hypothesized that tACS would increase RSA.Methods: A randomized, crossover, double-blind, sham-controlled pilot study was conducted to investigate the effects of 10Hz-tACS on metrics of ANS balance calculated from electrocardiogram (ECG). ECG data were collected for 2 mins before and after 40 mins of 10Hz-tACS or sham stimulation.Results: There were no significant changes in RSA or other frequency-domain HRV components from 10Hz-tACS. However, exploratory time-domain HRV analyses revealed a significant increase in the standard deviation of normal intervals between R-peaks (SDNN), a measure of ANS balance, for 10Hz-tACS relative to sham.Conclusion: Although tACS did not significantly increase RSA, we found in an exploratory analysis that tACS modulated an integrated HRV measure of both ANS branches. These findings support the further study of how the ANS and alpha oscillations interact and are modulated by tACS.ClinicalTrials.gov: Transcranial Alternating Current Stimulation in Back Pain – Pilot Study, NCT03243084.Keywords: low back pain, autonomic nervous system, heart rate variability, transcranial alternating current stimulation

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

Computational modeling and human studies suggest that transcranial alternating current stimulation (tACS) modulates alpha oscillations by entrainment. Yet, a direct examination of how tACS interacts with neuronal spiking activity that gives rise to the alpha oscillation in the thalamo-cortical system has been lacking. Here, we demonstrate how tACS entrains endogenous alpha oscillations in head-fixed awake ferrets. We first show that endogenous alpha oscillations in the posterior parietal cortex drive the primary visual cortex and the higher-order visual thalamus. Spike-field coherence is largest for the alpha frequency band, and presumed fast-spiking inhibitory interneurons exhibit strongest coupling to this oscillation. We then apply alpha-tACS that results in a field strength comparable to what is commonly used in humans (, Transcranial alternating current stimulation (tACS) can modulate cortical oscillations and associated long-lasting cognitive and behavioral functions in humans. Here, the authors provide in vivo evidence in ferrets on the mechanism of how weak electric fields in tACS can entrain neuronal activity.

Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing (AGB) technique that automates the process of combining the adversarial and pixel-wise terms and streamlines hyperparameter tuning. In addition, we introduce a Densely Connected Iterative Network, which is an undersampled MRI reconstruction network that utilizes dense connections. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques. To demonstrate the general nature of our method, it is further evaluated on a battery of image-to-image translation experiments, demonstrating an ability to recover from sub-optimal weighting in multi-term adversarial training., arXiv admin note: substantial text overlap with arXiv:1905.00985

OBJECTIVES Persistent oscillating vertigo that occurs after entrainment to periodic motion is known as Mal de Debarquement Syndrome (MdDS). Down-modulation of this oscillating vertigo is associated with reduction in long-range resting-state functional connectivity between fronto-parieto-occipital regions. In order to determine the association between this oscillating vertigo and hypersynchrony as measured by the auditory steady-state response (ASSR), we investigated the differences in ASSR between individuals with MdDS and healthy controls as well as the change in ASSR in individuals with MdDS before and after treatment with transcranial alternating current stimulation (tACS). MATERIALS AND METHODS Individuals with treatment refractory MdDS lasting at least six months received single administrations of fronto-parieto-occipital tACS in an "n-of-1" double-blind randomized design: alpha-frequency in-phase, alpha-frequency antiphase, and gamma frequency antiphase control. The treatment protocol that led to the most acute reduction in symptoms and improved balance was administered for 10-12 sessions given over three days (each session 20-min at 2-4 mA). RESULTS Twenty-four individuals with MdDS participated (mean age 53.0 ± 11.8 years [range: 22-66 years, median: 57.0 years]; mean duration of illness 38.6 ± 53.4 months [range: 6-240 months, median: 18.0 months]). Individuals with MdDS had elevated ASSR compared to healthy controls at baseline (t11 = 5.95, p

Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many methods have been employed to ameliorate the effects of patient motion, these often fall short in practice. In this paper we propose a novel method for detecting and timing patient motion during an MR scan and correcting for the motion artefacts using a deep neural network. The deep neural network contains two input branches that discriminate between patient poses using the motion’s timing. The first branch receives a subset of the k-space data collected during a single dominant patient pose, and the second branch receives the remaining part of the collected k-space data. The proposed method can be applied to artefacts generated by multiple movements of the patient. Furthermore, it can be used to correct motion for the case where k-space has been under-sampled to shorten the scan time, as is common when using methods such as parallel imaging or compressed sensing. Experimental results on both simulated and real MRI data show the efficacy of our approach.

Transcranial static magnetic field stimulation (tSMS) is a novel non-invasive brain stimulation technique that has been shown to locally increase alpha power in the parietal and occipital cortex. We investigated if tSMS locally increased alpha power in the left or right prefrontal cortex, as the balance of left/right prefrontal alpha power (frontal alpha asymmetry) has been linked to emotional processing and mood disorders. Therefore, altering frontal alpha asymmetry with tSMS may serve as a novel treatment to psychiatric diseases. We performed a crossover, double-blind, sham-controlled pilot study to assess the effects of prefrontal tSMS on neural oscillations. Twenty-four right-handed healthy participants were recruited and received left dorsolateral prefrontal cortex (DLPFC) tSMS, right DLPFC tSMS, and sham tSMS in a randomized order. Electroencephalography data were collected before (2 minutes eyes-closed, 2 minutes eyes-open), during (10 minutes eyes-open), and after (2 minutes eyes-open) stimulation. In contrast to our hypothesis, neither left nor right tSMS locally increased frontal alpha power. However, alpha power increased in occipital cortex during left DLPFC tSMS. Right DLPFC tSMS increased post-stimulation fronto-parietal theta power, indicating possible relevance to memory and cognition. Left and right DLPFC tSMS increased post-stimulation left hemisphere beta power, indicating possible changes to motor behavior. Left DLPFC tSMS also increased post-stimulation right frontal beta power, demonstrating complex network effects that may be relevant to aggressive behavior. We concluded that DLPFC tSMS modulated the network oscillations in regions distant from the location of stimulation and that tSMS has region specific effects on neural oscillations.

Introduction/Objective There is no reliable biomarker to predict the degree of fibrosis in CD. Quantification of C-mi has been used as a surrogate for enteric neural structures in Hirschsprung disease. The quantity of C-mi at the proximal margin may correlate with the degree of fibrosis in resected CD. Methods Ileocolonic resection cases for 20 CD and 3 trauma (control) were retrieved. Cases with severe mucosal inflammation at the margins were excluded. The proximal and distal margin sections were subjected to calretinin immunohistochemistry. Random mucosal images were captured from scanned slides (x200, JPEG), submucosa was edited out, and C-mi was calculated by image processing and analysis. rC-mi was defined as the mean C-mi of proximal margin normalized by that of distal margin. Cases with rC-mi less than the mean rC-mi + 2 SD of the controls were excluded. The maximum thicknesses of submucosa and muscularis propria were measured at the most stenotic site, microscopically. Pearson’s correlation test and Student’s t-test were performed to correlate the parameters and compare the means, respectively. Results A total of 216 images were captured (mean 9.3 images per case; range 7 to 10). The mean rC-mi of CD (1.26) was greater than the controls (mean 0.43) (p Conclusion The rC-mi of CD was inversely correlated with submucosal fibrosis. Altered stromal integrity, impaired intercellular signaling, progressive reduction and loss of telocytes induced by submucosal fibrosis may reduce the regenerative capacity of enteric neural structures in CD. Therefore, a decreasing trend in rC-mi may predict fibrosis progression in CD.

Model observers that replicate human observers are useful tools for assessing image quality based on detection tasks. Linear model observers including nonprewhitening matched filters (NPWMFs) and channelized Hotelling observers (CHOs) have been widely studied and applied successfully to evaluate and optimize detection performance. However, there is still room for improvement in predicting human observer responses in detection tasks. In this study, we used a convolutional neural network to predict human observer responses in a two-alternative forced choice (2AFC) task for PET imaging. Lesion-absent and lesion-present images were reconstructed from clinical PET data with simulated lesions added to the liver and lungs and were used for the 2AFC task. We trained the convolutional neural network to discriminate images that human observers chose as lesion-present and lesion-absent in the 2AFC task. We evaluated the performance of the trained network by calculating the concordance between human observer responses and predicted responses from the network output and compared it to those of NPWMF and CHO. The trained network showed better agreement with human observers than the linear NPWMF and CHO model observers. The results demonstrate the potential for convolutional neural networks as model observers that better predict human performance. Such model observers can be used for optimizing scanner design, imaging protocols, and image reconstruction to improve lesion detection in PET imaging.

The neuroactive metabolites of the steroid hormones progesterone (P4) and testosterone (T) are GABAergic modulators that influence cognitive control, yet the specific effect of P4 and T on brain network activity remains poorly understood. Here, we investigated if a fundamental oscillatory network activity pattern related to cognitive control, frontal midline theta (FMT) oscillations, are modulated by steroids hormones, P4 and T. We measured the concentration P4 and T using salivary enzyme immunoassay and FMT oscillations using high-density electroencephalography (EEG) during the eyes-open resting state in fifty-five healthy female and male participants. Electrical brain activity was analyzed using Morlet wavelet convolution, beamformer source localization, background noise spectral fitting, and phase amplitude coupling analysis. Steroid hormone concentrations and biological sex were used as predictors for scalp and source-estimated theta oscillations and for top-down theta-gamma phase amplitude coupling. Elevated concentrations of P4 predicted increased FMT oscillatory amplitude across both sexes, and no relationship was found with T. The positive correlation with P4 was specific to the frontal-midline electrodes and survived correction for the background noise of the brain. Using source localization, FMT oscillations were localized to the frontal-parietal network. Additionally, theta amplitude within the frontal-parietal network, but not the default mode network, positively correlated with P4 concentration. Finally, P4 concentration correlated with increased coupling between FMT phase and posterior gamma amplitude. Our results suggest that P4 concentration modulates brain activity via upregulation of theta oscillations in the frontal-parietal network and increased top-down control over posterior cortical sites.Significance StatementThe neuroactive metabolites of the steroid hormones progesterone (P4) and testosterone (T) are GABAergic modulators that influence cognitive control, yet the specific effect of P4 and T on brain network activity remains poorly understood. Here, we investigated if a fundamental oscillatory network activity pattern related to cognitive control, frontal midline theta (FMT) oscillations, are modulated by steroids hormones, P4 and T. Our results suggest that P4 concentration modulates brain activity via upregulation of theta oscillations in the frontal-parietal network and increased top-down control over posterior cortical sites.

Face perception is a highly developed function of the human visual system. Previous studies of event-related potentials (ERPs) have identified a face-selective ERP component (negative peak at about 170 milliseconds after stimulation onset, N170) in healthy participants. In contrast, patients with schizophrenia exhibit reduced amplitude of the N170, which may represent a pathological deficit in the neurophysiology of face perception. Interestingly, healthy humans with schizophrenia-like experiences (schizotypy) also exhibit abnormal processing of face perception. Yet, it has remained unknown how schizotypy in healthy humans is associated with the neurophysiological substrate of face perception. Here, we recruited 35 participants and assessed their schizotypy by the magical ideation rating scale. We used high-density electroencephalography to obtain ERPs elicited by a set of Mooney faces (face and non-face conditions). We divided the participants into two groups (high and low schizotypy) by a median split of schizotypy scores. We investigated mean reaction times and the N170 component in response to the stimuli. We found significant slowed reaction times and reduced amplitude of the N170 component in response to the face stimuli in the high-schizotypy group. In addition, across the full data set, we found that the schizotypy scores were significantly correlated with both the reaction times and the N170 amplitude. Our results thus support the model of schizotypy as a manifestation of a continuum between healthy individuals and patients with schizophrenia, where the N170 impairment serves as a biomarker for the degree of pathology along this continuum.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncertainties due to the limitations of MRI contrast (e.g. accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that, in addition to generating an initial pseudo-CT from MR data, also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with MLAA reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher RMSE than Zero-echo-time and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions., Comment: Accepted to the IEEE Transactions on Radiation and Plasma Medical Sciences on October 3, 2021. To be published under open access Creative Commons Attribution License (CC BY)

In this study, we explore the use of a spatially-variant penalty strength in penalized image reconstruction using anatomical priors to reduce the dependence of lesion contrast on surrounding activity and lesion location. This work builds on a previous method to make the local perturbation response (LPR) approximately spatially invariant. While the dependence of lesion contrast on the local properties introduced by the anatomical penalty is intentional, the method aims to reduce the influence from surroundings lying along the lines of response (LORs) but not in the penalty neighborhood structure. The method is evaluated using simulated data, assuming that the anatomical information is absent or well-aligned with the corresponding activity images. Since the parallel level sets (PLS) penalty is convex and has shown promising results in the literature, it is chosen as the representative anatomical penalty and incorporated into the previously proposed preconditioned algorithm (L-BFGS-B-PC) for achieving good image quality and fast convergence rate. A 2D disc phantom with a feature at the center and a 3D XCAT thorax phantom with lesions inserted in different slices are used to study how surrounding activity and lesion location affect the visual appearance and quantitative consistency. A bias and noise analysis is also performed with the 2D disc phantom. The consistency of the algorithm convergence rate with respect to different data noise and background levels is also investigated using the XCAT phantom. Finally, an example of reconstruction for a patient dataset with inserted pseudo lesions is used as a demonstration in a clinical context. We show that applying the spatially-variant penalization with PLS can reduce the dependence of the lesion contrast on the surrounding activity and lesion location. It does not affect the bias and noise trade-off curves for matched local resolution. Moreover, when using the proposed penalization, significant improvement in algorithm convergence rate and convergence consistency is observed. ispartof: IEEE TRANSACTIONS ON MEDICAL IMAGING vol:39 issue:1 pages:11-22 ispartof: location:United States status: published

Background Single-pulse transcranial magnetic stimulation (TMS) elicits an evoked electroencephalography (EEG) potential (TMS-evoked potential, TEP), which is interpreted as direct evidence of cortical reactivity to TMS. Thus, combining TMS with EEG can be used to investigate the mechanism underlying brain network engagement in TMS treatment paradigms. However, controversy remains regarding whether TEP is a genuine marker of TMS-induced cortical reactivity or if it is confounded by responses to peripheral somatosensory and auditory inputs. Resolving this controversy is of great significance for the field and will validate TMS as a tool to probe networks of interest in cognitive and clinical neuroscience. Objective Here, we delineated the cortical origin of TEP by spatially and temporally localizing successive TEP components, and modulating them with transcranial direct current stimulation (tDCS) to investigate cortical reactivity elicited by single-pulse TMS and its causal relationship with cortical excitability. Methods We recruited 18 healthy participants in a double-blind, cross-over, sham-controlled design. We collected motor-evoked potentials (MEPs) and TEPs elicited by suprathreshold single-pulse TMS targeting the left primary motor cortex (M1). To causally test cortical and corticospinal excitability, we applied tDCS to the left M1. Results We found that the earliest TEP component (P25) was localized to the left M1. The following TEP components (N45 and P60) were largely localized to the primary somatosensory cortex, which may reflect afferent input by hand-muscle twitches. The later TEP components (N100, P180, and N280) were largely localized to the auditory cortex. As hypothesized, tDCS selectively modulated cortical and corticospinal excitability by modulating the pre-stimulus mu-rhythm oscillatory power. Conclusion Together, our findings provide causal evidence that the early TEP components reflect cortical reactivity to TMS.

It is important to predict and understand how scanner design characteristics will affect image quality relevant to clinical tasks when developing a new system. It is a complex task to understand the interplay among multiple system parameters such as time-of-flight (TOF) resolution, sensitivity, intrinsic spatial resolution, and depth-of-interaction (DOI) resolution. This can be achieved by Monte Carlo (MC) simulation. Here we perform MC simulation using SimSET to simulate brain-dedicated positron emission tomography (PET) scanners with varying system parameters such as DOI resolution, intrinsic spatial resolution and TOF resolution. We report preliminary results from simulating various phantoms. The results show that DOI information greatly improves image resolution particularly in the peripheral regions, reducing parallax errors even without point spread function modeling, that smaller detector elements improve image resolution, and that TOF information lowers image noise. The MC simulation analysis provides useful results for optimizing detector geometry and hardware for high resolution neuro-PET scanner design.

Statistical PET image reconstruction methods are often accelerated by the use of a subset of available projections at each iteration. It is known that many subset algorithms, such as ordered subset expectation maximisation, will not converge to a single solution but to a limit cycle. Reconstruction methods exist to relax the update step sizes of subset algorithms to obtain convergence, however, this introduces additional parameters that may result in extended reconstruction times. Another approach is to gradually decrease the number of subsets to reduce the effect of the limit cycle at later iterations, but the optimal iteration numbers for these reductions may be data dependent. We propose an automatic method to increase subset sizes so a reconstruction can take advantage of the acceleration provided by small subset sizes during early iterations, while at later iterations reducing the effects of the limit cycle behaviour providing estimates closer to the maximum a posteriori solution. At each iteration, two image updates are computed from a common estimate using two disjoint subsets. The divergence of the two update vectors is measured and, if too great, subset sizes are increased in future iterations. We show results for both sinogram and list mode data using various subset selection methodologies.

The aganglionic segment of bowel in Hirschsprung’s disease (HD) varies in length. It is not clear whether total colonic aganglionosis (TCA) merely represents a long form of HD or a different phenotype of the disease. Animal model studies suggest that TCA may have a longer transition zone (TZ) than conventional colorectal HD. We compared mucosal innervation of TZ in 2 TCA cases and 10 conventional colorectal HD cases by quantifying calretinin-positive mucosal nerve fibers using image processing and analysis. One TCA was associated with esophageal atresia-tracheoesophageal fistula, the other with trisomy 21. The gradients of calretinin-stained pixel count increase per distance from the beginning of TZ (slope) for TCA were not significantly different from those for the conventional HD group. Given this observation, it is speculated that the length of TZ in TCA may fall within the range of and may not be much longer than conventional colorectal HD.

Both geomagnetic and solar activity fluctuate over time and have been proposed to affect human physiology. Heart rate variability (HRV) has substantial health implications regarding the ability to adapt to stressors and has been shown to be altered in many cardiovascular and neurological disorders. Intriguingly, previous work found significant, strong correlations between HRV and geomagnetic/solar activity. The purpose of this study to replicate these findings. We simultaneously measured HRV during a 30-day period.We recruited 20 healthy participants and measured their HRV for a 30-day period. We also collected geomagnetic and solar activity during this period for investigating their relationship with the HRV data.In agreement with previous work, we found several significant correlations between short-term HRV and geophysical time-series. However, after correction for autocorrelation, which is inherent in time-series, the only significant results were an increase in very low frequency during higher local geomagnetic activity and a geomagnetic anticipatory decrease in heart rate a day before the higher global geomagnetic activity. Both correlations were very low. The loss of most significant effects after this correction suggests that previous findings may be a result of autocorrelation. A further note of caution is required since our and the previous studies in the field do not correct for multiple comparisons given the exploratory analysis strategy.We thus conclude that the effects of geomagnetic and solar activity are (if present) most likely of very small effect size and we question the validity of the previous studies given the methodological concerns we have uncovered with our work.

Our objective was to determine clinically the value of time-of-flight (TOF) information in reducing PET artifacts and improving PET image quality and accuracy in simultaneous TOF PET/MR scanning. A total 65 patients who underwent a comparative scan in a simultaneous TOF PET/MR scanner were included. TOF and non-TOF PET images were reconstructed, clinically examined, compared and scored. PET imaging artifacts were categorized as large or small implant-related artifacts, as dental implant-related artifacts, and as implant-unrelated artifacts. Differences in image quality, especially those related to (implant) artifacts, were assessed using a scale ranging from 0 (no artifact) to 4 (severe artifact). A total of 87 image artifacts were found and evaluated. Four patients had large and eight patients small implant-related artifacts, 27 patients had dental implants/fillings, and 48 patients had implant-unrelated artifacts. The average score was 1.14 ± 0.82 for non-TOF PET images and 0.53 ± 0.66 for TOF images (p

Background/Aims: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. Methods: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. Results: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. Conclusion: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

Supplemental Material for Transition Zone in Total Colonic Aganglionosis and Colorectal Hirschsprung’s Disease Shows a Similar Trend of Mucosal Innervation: Image Processing and Analysis Study by Fernanda Cordeiro-Rudnisky, Sangtae Ahn, Natallia Sheuka, Christine Whyte, Ann Boguniewicz, Rong Fan, Jingmei Lin, Timothy A Jennings and Hwajeong Lee in Pediatric and Developmental Pathology

In dual-modality systems, using anatomical priors has been shown to improve image quality and quantification in emission tomography. However, alignment between the functional and anatomical images is crucial. In this study, we propose two algorithms for solving misalignment issues. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation uses alternates between image reconstruction and alignment estimation. To evaluate the potential of these approaches, we have chosen Parallel Level Sets (PLS) as a representative anatomical penalty since it has shown promising results in literature, incorporating a spatially-variant penalty strength to achieve uniform local contrast and fast convergence rate. The performance evaluation was achieved by using simulated non-TOF data generated with an XCAT phantom in the thorax region. We used the attenuation image in the anatomical prior. The results demonstrated that both methods are able to estimate the misalignment and deform the anatomical image accordingly when a proper workflow for the alternating optimization is applied. However, the performance of the first approach depends highly on the workflow of the alternating process. In contrast, the second approach shows the ability to converge to the correct alignment faster than the first approach does, independent of the workflow. Our results indicate that it is possible to align functional and anatomical information, enabling the use of anatomical priors in practice.

Transcranial alternating current stimulation (tACS) modulates endogenous neural oscillations in healthy human participants by the application of a low-amplitude electrical current with a periodic stimulation waveform. Yet, it is unclear if tACS can modulate and restore neural oscillations that are reduced in patients with psychiatric illnesses such as schizophrenia. Here, we asked if tACS modulates network oscillations in schizophrenia. We performed a randomized, double-blind, sham-controlled clinical trial to contrast tACS with transcranial direct current stimulation (tDCS) and sham stimulation in 22 schizophrenia patients with auditory hallucinations. We used high-density electroencephalography to investigate if a five-day, twice-daily 10Hz-tACS protocol enhances alpha oscillations and modulates network dynamics that are reduced in schizophrenia. We found that 10Hz-tACS enhanced alpha oscillations and modulated functional connectivity in the alpha frequency band. In addition, 10Hz-tACS enhanced the 40Hz auditory steady-state response (ASSR), which is reduced in patients with schizophrenia. Importantly, clinical improvement of auditory hallucinations correlated with enhancement of alpha oscillations and the 40Hz-ASSR. Together, our findings suggest that tACS has potential as a network-level approach to modulate reduced neural oscillations related to clinical symptoms in patients with schizophrenia.

This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.

Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user’s sense of the motor imagery process and directly estimate MI-BCI performance through the user’s self-prediction are lacking. In this study, we first test each user’s self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject’s self-prediction of MI-BCI performance. The subjects’ performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

Purpose Quantification of calretinin-stained mucosal nerve fibers by image processing and analysis (IPA) may objectively define the transition zone (TZ) of Hirschsprung disease (HD). We tested the utility of IPA as an adjunctive tool in HD. Materials and Methods Calretinin immunostain was performed on 15 HD pull-through specimens, and multiple images were captured from the proximal aganglionic zone, TZ, and probable normal zone (NZ). Pixel count (PC), defined as the percentage of brown-stained pixels in the mucosa, was quantified and plotted against distance from the rectal distal end. To validate the method, PCs from 45 images were compared with three-tiered visual scoring by five pathologists. Results were correlated against pertinent variables, which were retrieved from the clinical record. Results The PC gradually increased in the TZ toward the proximal resection margin in 10/13 (77%) cases. The PC variation in the probable NZ and around the circumference was substantial by the coefficient of variation. The mean PC of images with a visual score of 1 was less than scores of 2 and 3 by all five (100%) pathologists (p Conclusion While the mucosal calretinin staining gradually increases in the TZ, for now, the boundaries of the TZ cannot be accurately defined by mucosal biopsies given the substantial variation of staining around the circumference at the same distance and in the NZ. However, the IPA technique does provide a continuous variable and warrants further utility in HD studies.

Recently, neurophysiological findings about social interaction have been investigated widely, and hardware has been developed that can measure multiple subjects' brain activities simultaneously. These hyperscanning studies have enabled us to discover new and important evidences of interbrain interactions. Yet, very little is known about verbal interaction without any visual input. Therefore, we conducted a new hyperscanning study based on verbal, interbrain turn-taking interaction using simultaneous EEG/MEG, which measures rapidly changing brain activities. To establish turn-taking verbal interactions between a pair of subjects, we set up two EEG/MEG systems (19 and 146 channels of EEG and MEG, respectively) located ∼100 miles apart. Subjects engaged in verbal communication via condenser microphones and magnetic-compatible earphones, and a network time protocol synchronized the two systems. Ten subjects participated in this experiment and performed verbal interaction and noninteraction tasks separately. We found significant oscillations in EEG alpha and MEG alpha/gamma bands in several brain regions for all subjects. Furthermore, we estimated phase synchronization between two brains using the weighted phase lag index and found statistically significant synchronization in EEG and MEG data. Our novel paradigm and neurophysiological findings may foster a basic understanding of the functional mechanisms involved in human social interactions. Hum Brain Mapp 39:171-188, 2018. © 2017 Wiley Periodicals, Inc.

Purpose To investigate proton density (PD)-weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures. Methods A rotating ultra-fast imaging sequence (RUFIS) type ZT pulse sequence was developed and optimized for 1) efficient capture of short T2 bone signals and 2) flat PD response for soft-tissues. An inverse logarithmic image scaling (i.e., −log(image)) was used to highlight bone and differentiate it from surrounding soft-tissue and air. Furthermore, a histogram-based bias-correction method was developed for subsequent threshold-based air, soft-tissue, and bone segmentation. Results PD-weighted ZT imaging in combination with an inverse logarithmic scaling was found to provide excellent depiction of cranial bone structures. In combination with bias correction, also excellent segmentation results were achieved. A two-dimensional histogram analysis demonstrates a strong, approximately linear correlation between inverse log-scaled ZT and low-dose CT for Hounsfield units (HU) between −300 HU and 1,500 HU (corresponding to soft-tissue and bone). Conclusions PD-weighted ZT imaging provides robust and efficient depiction of bone structures in the head, with an excellent contrast between air, soft-tissue, and bone. Besides structural bone imaging, the presented method is expected to be of relevance for attenuation correction in positron emission tomography (PET)/MR and MR-based radiation therapy planning. Magn Reson Med 75:107–114, 2016. © 2015 Wiley Periodicals, Inc.

Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality’s drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features. In addition, mismatch of recording locations may hinder further improvement. In this literature review, we surveyed studies of the integration of EEG/fNIRS in BCI thoroughly and discussed its current limitations. We also suggested future directions for efficient and successful multi-modal integration of EEG/fNIRS in BCI systems.

This study explores the use of a spatially-variant penalty strength, proposed initially for quadratic penalties, in penalized image reconstruction using anatomical information. We have used the recently proposed Parallel Level Sets (PLS) anatomical prior as it has shown promising results in the literature. It was incorporated into the previously proposed preconditioned algorithm (L-BFGS-B-PC) for achieving both good image quality and fast convergence rate. A 2-dimensional (2D) disc phantom with a hot spot at the center and a 3D XCAT thorax phantom with lesions inserted in different slices are used to study how surrounding activity and lesion location affect both the visual appearance and quantitative consistency, respectively. Anatomical information is provided and assumed to be well-aligned with the corresponding activity images. For the XCAT phantom, the inserted lesions are either present or absent in the anatomical images to investigate the influence of the anatomical penalty. The reconstructed images for both phantoms with and without applying the spatially-variant penalty strength are compared. Preliminary results demonstrate that applying the spatially-variant penalization with an anatomical prior can reduce the dependence of local contrast on background activity and lesion location. Further work to explore the potential benefit in clinical imaging is warranted.

Projection data acquired from a positron emission tomography (PET) scanner consist of true, scattered and random events. Scattered events can cause severe artifacts and quantitation errors in reconstructed PET images unless corrected for properly. A scatter correction algorithm is required to predict scattered events from the measurement. Scatter correction requires estimation of both single scatter and multiple scatter profiles. Usually, single scatter profiles are calculated by modelbased simulation and multiple scatter profiles are estimated by a kernel-based convolution method. However, design of the convolution kernels for multiple scatter estimation is sophisticated and requires fine parameter tuning. In this work, we adopt deep learning techniques for scatter estimation. We propose two convolutional neural networks. The first network estimates multiple scatter profiles from single scatter profiles, replacing the kernel-based convolution method. The second network is designed to predict the total scatter profiles (including single and multiple scatters) directly from the input of emission and attenuation sinograms. Initial results from both networks show a promise with the potential for more accurate and faster scatter correction for PET.

A penalized-likelihood (PL) image reconstruction algorithm using the relative difference penalty (RDP), called a PL-RDP algorithm, has recently been developed for clinical PET and has previously demonstrated improved Iesion quantitation accuracy and Iesion detectability over the current clinical standard PET image reconstruction algorithm, ordered subsets expectation maximization (OSEM). Patient size is one of the main factors affecting PET image quality, but the effects of patient size on PL-RDP have not been investigated. The purpose of this study was to evaluate the impact of patient size on image quality for PL-RDP compared to OSEM. We inserted simulated liver Sesions with varying sizes and contrasts into clinical data. To evaluate Iesion detectability, we applied a channelized Hotelling observer and calculated the signal-to-noise ratio (SNR). To evaluate Iesion quantitation accuracy, we compared contrast recovery coefficient (CRC) at matched image roughness, which is a surrogate image noise measure. The Iesion detectability and the quantitation accuracy in a group of smaller-sized patients with body mass index (BMI) 26 kg/m2. The detection performance of TOF PL-RDP was better than that of TOF OSEM for 20 mm Iesions with contrast $\geq 0.5$ in the smaller-sized patient group; and for 10 mm Iesion with contrast $\geq$ 1.25, 15 mm Iesions with contrast $\geq 0.5$ and 20 mm Iesions with contrast $\geq$ 0.25 in the larger-sized patient group. The detectability for non-TOF PL-RDP was better than that for non- TOF OSEM for 20 mm Iesions with contrast $\geq 0.5$ in the Iarger- sized patient group. The quantitation performance of non-TOF PL-RDP was better than that of non-TOF OSEM for 20 mm Iesions with contrast $\geq 0.5$, 15 mm Iesions with contrast $\geq 1$, and 10 mm Iesions with contrast $\geq 4$ in the smaller-sized patient group; and for 20 mm Iesions with contrast $\geq 0.5$, 15 mm Iesions with contrast $\geq 0.5$, and 10 mm Iesions with contrast $\geq 4$ in the larger-sized patient group. In addition, the improvement of PL- RDP in detection over OSEM for the larger-sized patients was larger than that for the smaller-sized patients for 15 mm Iesion with contrast $\geq 0.75$ and 20 mm Iesions with contrast $\geq 0.5$ in the TOF case; and the improvement of PL-RDP in quantitation accuracy over OSEM for the larger-sized patients was larger than that for the smaller-sized patients for 20 mm Sesions with contrast $\geq 0.5$ in the non-TOF case.

Due to the recent explosion in various forms of 3D content, the evaluation of such content from a neuroscience perspective is quite interesting. However, existing investigations of cortical oscillatory responses in stereoscopic depth perception are quite rare. Therefore, we investigated spatiotemporal and spatio-temporo-spectral features at four different stereoscopic depths within the comfort zone. We adopted a simultaneous EEG/MEG acquisition technique to collect the oscillatory responses of eight participants. We defined subject-specific retinal disparities and designed a single trial-based stereoscopic viewing experimental paradigm. In the group analysis, we observed that, as the depth increased from Level 1 to Level 3, there was a time-locked increase in the N200 component in MEG and the P300 component in EEG in the occipital and parietal areas, respectively. In addition, initial alpha and beta event-related desynchronizations (ERD) were observed at approximately 500 to 1000 msec, while theta, alpha, and beta event-related synchronizations (ERS) appeared at approximately 1000 to 2000 ms. Interestingly, there was a saturation point in the increase in cognitive responses, including N200, P300, and alpha ERD, even when the depth increased only within the comfort zone. Meanwhile, the magnitude of low beta ERD decreased in the dorsal pathway as depth increased. From these findings, we concluded that cognitive responses are likely to become saturated in the visual comfort zone, while perceptual load may increase with depth.

Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit).