Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology.

OBJECTIVES: To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements. METHODS: Semi-structured interviews were conducted with twenty-five respondents-nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding. RESULTS: We identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements. CONCLUSIONS: Development of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements. CLINICAL RELEVANCE STATEMENT: For AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field. KEY POINTS: • Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility. •Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem's inherent complexity by finding and promoting well-defined solutions.

From MEG to clinical EEG: evaluating a promising non-invasive estimator of defense-related muscle sympathetic nerve inhibition.

Sudden, unexpected stimuli can induce a transient inhibition of sympathetic vasoconstriction to skeletal muscle, indicating a link to defense reactions. This phenomenon is relatively stable within, but differs between, individuals. It correlates with blood pressure reactivity which is associated with cardiovascular risk. Inhibition of muscle sympathetic nerve activity (MSNA) is currently characterized through invasive microneurography in peripheral nerves. We recently reported that brain neural oscillatory power in the beta spectrum (beta rebound) recorded with magnetoencephalography (MEG) correlated closely with stimulus-induced MSNA inhibition. Aiming for a clinically more available surrogate variable reflecting MSNA inhibition, we investigated whether a similar approach with electroencephalography (EEG) can accurately gauge stimulus-induced beta rebound. We found that beta rebound shows similar tendencies to correlate with MSNA inhibition, but these EEG data lack the robustness of previous MEG results, although a correlation in the low beta band (13-20 Hz) to MSNA inhibition was found (p = 0.021). The predictive power is summarized in a receiver-operating-characteristics curve. The optimum threshold yielded sensitivity and false-positive rate of 0.74 and 0.33 respectively. A plausible confounder is myogenic noise. A more complicated experimental and/or analysis approach is required for differentiating MSNA-inhibitors from non-inhibitors based on EEG, as compared to MEG.

Gamma oscillations point to the role of primary visual cortex in atypical motion processing in autism.

Neurophysiological studies suggest that abnormal neural inhibition may explain a range of sensory processing differences in autism spectrum disorders (ASD). In particular, the impaired ability of people with ASD to visually discriminate the motion direction of small-size objects and their reduced perceptual suppression of background-like visual motion may stem from deficient surround inhibition within the primary visual cortex (V1) and/or its atypical top-down modulation by higher-tier cortical areas. In this study, we estimate the contribution of abnormal surround inhibition to the motion-processing deficit in ASD. For this purpose, we used a putative correlate of surround inhibition-suppression of the magnetoencephalographic (MEG) gamma response (GR) caused by an increase in the drift rate of a large annular high-contrast grating. The motion direction discrimination thresholds for the gratings of different angular sizes (1° and 12°) were assessed in a separate psychophysical paradigm. The MEG data were collected in 42 boys with ASD and 37 typically developing (TD) boys aged 7-15 years. Psychophysical data were available in 33 and 34 of these participants, respectively. The results showed that the GR suppression in V1 was reduced in boys with ASD, while their ability to detect the direction of motion was compromised only in the case of small stimuli. In TD boys, the GR suppression directly correlated with perceptual suppression caused by increasing stimulus size, thus suggesting the role of the top-down modulations of V1 in surround inhibition. In ASD, weaker GR suppression was associated with the poor directional sensitivity to small stimuli, but not with perceptual suppression. These results strongly suggest that a local inhibitory deficit in V1 plays an important role in the reduction of directional sensitivity in ASD and that this perceptual deficit cannot be explained exclusively by atypical top-down modulation of V1 by higher-tier cortical areas.

Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.

OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient. METHODS: Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases. RESULTS: We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases. CONCLUSIONS: Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.

Globally elevated excitation-inhibition ratio in children with autism spectrum disorder and below-average intelligence.

BACKGROUND: Altered neuronal excitation-inhibition (E-I) balance is strongly implicated in ASD. However, it is not known whether the direction and degree of changes in the E-I ratio in individuals with ASD correlates with intellectual disability often associated with this developmental disorder. The spectral slope of the aperiodic 1/f activity reflects the E-I balance at the scale of large neuronal populations and may uncover its putative alternations in individuals with ASD with and without intellectual disability. METHODS: Herein, we used magnetoencephalography (MEG) to test whether the 1/f slope would differentiate ASD children with average and below-average (< 85) IQ. MEG was recorded at rest with eyes open/closed in 49 boys with ASD aged 6-15 years with IQ ranging from 54 to 128, and in 49 age-matched typically developing (TD) boys. The cortical source activity was estimated using the beamformer approach and individual brain models. We then extracted the 1/f slope by fitting a linear function to the log-log-scale power spectra in the high-frequency range. RESULTS: The global 1/f slope averaged over all cortical sources demonstrated high rank-order stability between the two conditions. Consistent with previous research, it was steeper in the eyes-closed than in the eyes-open condition and flattened with age. Regardless of condition, children with ASD and below-average IQ had flatter slopes than either TD or ASD children with average or above-average IQ. These group differences could not be explained by differences in signal-to-noise ratio or periodic (alpha and beta) activity. LIMITATIONS: Further research is needed to find out whether the observed changes in E-I ratios are characteristic of children with below-average IQ of other diagnostic groups. CONCLUSIONS: The atypically flattened spectral slope of aperiodic activity in children with ASD and below-average IQ suggests a shift of the global E-I balance toward hyper-excitation. The spectral slope can provide an accessible noninvasive biomarker of the E-I ratio for making objective judgments about treatment effectiveness in people with ASD and comorbid intellectual disability.

Brain structural and functional correlates to defense-related inhibition of muscle sympathetic nerve activity in man.

An individual's blood pressure (BP) reactivity to stress is linked to increased risk of hypertension and cardiovascular disease. However, inter- and intra-individual BP variability makes understanding the coupling between stress, BP reactivity, and long-term outcomes challenging. Previous microneurographic studies of sympathetic signaling to muscle vasculature (i.e. muscle sympathetic nerve activity, MSNA) have established a neural predictor for an individual's BP reactivity during short-lasting stress. Unfortunately, this method is invasive, technically demanding, and time-consuming and thus not optimal for widespread use. Potential central nervous system correlates have not been investigated. We used MagnetoEncephaloGraphy and Magnetic Resonance Imaging to search for neural correlates to sympathetic response profiles within the central autonomic network and sensorimotor (Rolandic) regions in 20 healthy young males. The main correlates include (a) Rolandic beta rebound and an anterior cingulate cortex (ACC) response elicited by sudden stimulation and (b) cortical thickness in the ACC. Our findings highlight the involvement of the ACC in reactions to stress entailing peripheral sympathetic responses to environmental stimuli. The Rolandic response furthermore indicates a surprisingly strong link between somatosensory and autonomic processes. Our results thus demonstrate the potential in using non-invasive neuroimaging-based measures of stress-related MSNA reactions, previously assessed only using invasive microneurography.

Visual gamma oscillations predict sensory sensitivity in females as they do in males.

Gamma oscillations are driven by local cortical excitatory (E)-inhibitory (I) loops and may help to characterize neural processing involving excitatory-inhibitory interactions. In the visual cortex reliable gamma oscillations can be recorded with magnetoencephalography (MEG) in the majority of individuals, which makes visual gamma an attractive candidate for biomarkers of brain disorders associated with E/I imbalance. Little is known, however, about if/how these oscillations reflect individual differences in neural excitability and associated sensory/perceptual phenomena. The power of visual gamma response (GR) changes nonlinearly with increasing stimulation intensity: it increases with transition from static to slowly drifting high-contrast grating and then attenuates with further increase in the drift rate. In a recent MEG study we found that the GR attenuation predicted sensitivity to sensory stimuli in everyday life in neurotypical adult men and in men with autism spectrum disorders. Here, we replicated these results in neurotypical female participants. The GR enhancement with transition from static to slowly drifting grating did not correlate significantly with the sensory sensitivity measures. These findings suggest that weak velocity-related attenuation of the GR is a reliable neural concomitant of visual hypersensitivity and that the degree of GR attenuation may provide useful information about E/I balance in the visual cortex.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

OBJECTIVES: Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN: Scoping review. SETTING: Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS: Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS: The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool.

OBJECTIVES: Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism. METHODS: The classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls. RESULTS: Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups. CONCLUSION: These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.

On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG.

Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20-40 â€‹mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 â€‹K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system. We compared the spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges. We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (10-20 â€‹ms). This corresponded to the electroencephalographic (EEG) component P16/N16 and was an unexpected observation as this component is usually not observed in conventional MEG. This finding shows that on-scalp MEG enables a richer registration of the cortical signal, indicating a sensitivity to what are potentially sources in the thalamo-cortical radiation. We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60-75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We show how the current sensor coverage may have limited our chances to register the necessary between-phalange source field dissimilarities for fair hypothesis testing, an approach we otherwise believe to be useful for future benchmarking measurements.

Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements.

OBJECTIVE: Conventional MEG provides an unsurpassed ability to, non-invasively, detect epileptic activity. However, highly resolved information on small neuronal populations required in epilepsy diagnostics is lost and can be detected only intracranially. Next-generation on-scalp magnetencephalography (MEG) sensors aim to retrieve information unavailable to conventional non-invasive brain imaging techniques. To evaluate the benefits of on-scalp MEG in epilepsy, we performed the first-ever such measurement on an epilepsy patient. METHODS: Conducted as a benchmarking study focusing on interictal epileptiform discharge (IED) detectability, an on-scalp high-temperature superconducting quantum interference device magnetometer (high-Tc SQUID) system was compared to a conventional, low-temperature SQUID system. Co-registration of electroencephalopraphy (EEG) was performed. A novel machine learning-based IED-detection algorithm was developed to aid identification of on-scalp MEG unique IEDs. RESULTS: Conventional MEG contained 24 IEDs. On-scalp MEG revealed 47 IEDs (16 co-registered by EEG, 31 unique to the on-scalp MEG recording). CONCLUSION: Our results indicate that on-scalp MEG might capture IEDs not seen by other non-invasive modalities. SIGNIFICANCE: On-scalp MEG has the potential of improving non-invasive epilepsy evaluation.

Spatial suppression in visual motion perception is driven by inhibition: Evidence from MEG gamma oscillations.

Spatial suppression (SS) is a visual perceptual phenomenon that is manifest in a reduction of directional sensitivity for drifting high-contrast gratings whose size exceeds the center of the visual field. Gratings moving at faster velocities induce stronger SS. The neural processes that give rise to such size- and velocity-dependent reductions in directional sensitivity are currently unknown, and the role of surround inhibition is unclear. In magnetoencephalogram (MEG), large high-contrast drifting gratings induce a strong gamma response (GR), which also attenuates with an increase in the gratings' velocity. It has been suggested that the slope of this GR attenuation is mediated by inhibitory interactions in the primary visual cortex. Herein, we investigate whether SS is related to this inhibitory-based MEG measure. We evaluated SS and GR in two independent samples of participants: school-age boys and adult women. The slope of GR attenuation predicted inter-individual differences in SS in both samples. Test-retest reliability of the neuro-behavioral correlation was assessed in the adults, and was high between two sessions separated by several days or weeks. Neither frequencies nor absolute amplitudes of the GRs correlated with SS, which highlights the functional relevance of velocity-related changes in GR magnitude caused by augmentation of incoming input. Our findings provide evidence that links the psychophysical phenomenon of SS to inhibitory-based neural responses in the human primary visual cortex. This supports the role of inhibitory interactions as an important underlying mechanism for spatial suppression.

On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration.

Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured at every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-Tc SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (≤ 2 â€‹mm, < 3° and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 â€‹mm and < 1°, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on co-registration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 â€‹mm) with those based on a commercial, whole-head MEG system.

Additive effect of contrast and velocity suggests the role of strong excitatory drive in suppression of visual gamma response.

It is commonly acknowledged that gamma-band oscillations arise from interplay between neural excitation and inhibition; however, the neural mechanisms controlling the power of stimulus-induced gamma responses (GR) in the human brain remain poorly understood. A moderate increase in velocity of drifting gratings results in GR power enhancement, while increasing the velocity beyond some 'transition point' leads to GR power attenuation. We tested two alternative explanations for this nonlinear input-output dependency in the GR power. First, the GR power can be maximal at the preferable velocity/temporal frequency of motion-sensitive V1 neurons. This 'velocity tuning' hypothesis predicts that lowering contrast either will not affect the transition point or shift it to a lower velocity. Second, the GR power attenuation at high velocities of visual motion can be caused by changes in excitation/inhibition balance with increasing excitatory drive. Since contrast and velocity both add to excitatory drive, this 'excitatory drive' hypothesis predicts that the 'transition point' for low-contrast gratings would be reached at a higher velocity, as compared to high-contrast gratings. To test these alternatives, we recorded magnetoencephalography during presentation of low (50%) and high (100%) contrast gratings drifting at four velocities. We found that lowering contrast led to a highly reliable shift of the GR suppression transition point to higher velocities, thus supporting the excitatory drive hypothesis. No effects of contrast or velocity were found in the alpha-beta range. The results have implications for understanding the mechanisms of gamma oscillations and developing gamma-based biomarkers of disturbed excitation/inhibition balance in brain disorders.

A 7-Channel High-[Formula: see text] SQUID-Based On-Scalp MEG System.

OBJECTIVE: To present the technical design and demonstrate the feasibility of a multi-channel on-scalp magnetoencephalography (MEG) system based on high critical temperature (high-[Formula: see text]) superconducting quantum interference devices (SQUIDs). METHODS: We built a liquid nitrogen-cooled cryostat that houses seven YBCO SQUID magnetometers arranged in a dense, head-aligned array with minimal distance to the room-temperature environment for all sensors. We characterize the performance of this 7-channel system in terms of on-scalp MEG utilization and present recordings of spontaneous and evoked brain activity. RESULTS: The center-to-center spacing between adjacent SQUIDs is 12.0 and 13.4 mm and all SQUIDs are in the range of 1-3 mm of the head surface. The cryostat reaches a base temperature of  âˆ¼ 70 K and stays cold for 16 h with a single 0.9 L filling. The white noise levels of the magnetometers is 50-130 fT/Hz1/2 at 10 Hz and they show low sensor-to-sensor feedback flux crosstalk ( 0.6%). We demonstrate evoked fields from auditory stimuli and single-shot sensitivity to alpha modulation from the visual cortex. CONCLUSION: All seven channels in the system sensitively sample neuromagnetic fields with mm-scale scalp standoff distances. The hold time of the cryostat furthermore is sufficient for a day of recordings. As such, our multi-channel high-[Formula: see text] SQUID-based system meets the demands of on-scalp MEG. SIGNIFICANCE: The system presented here marks the first high-[Formula: see text] SQUID-based on-scalp MEG system with more than two channels. It enables us to further explore the benefits of on-scalp MEG in future recordings.

Characterization of Binding of Magnetic Nanoparticles to Rolling Circle Amplification Products by Turn-On Magnetic Assay.

The specific binding of oligonucleotide-tagged 100 nm magnetic nanoparticles (MNPs) to rolling circle products (RCPs) is investigated using our newly developed differential homogenous magnetic assay (DHMA). The DHMA measures ac magnetic susceptibility from a test and a control samples simultaneously and eliminates magnetic background signal. Therefore, the DHMA can reveal details of binding kinetics of magnetic nanoparticles at very low concentrations of RCPs. From the analysis of the imaginary part of the DHMA signal, we find that smaller MNPs in the particle ensemble bind first to the RCPs. When the RCP concentration increases, we observe the formation of agglomerates, which leads to lower number of MNPs per RCP at higher concentrations of RCPs. The results thus indicate that a full frequency range of ac susceptibility observation is necessary to detect low concentrations of target RCPs and a long amplification time is not required as it does not significantly increase the number of MNPs per RCP. The findings are critical for understanding the underlying microscopic binding process for improving the assay performance. They furthermore suggest DHMA is a powerful technique for dynamically characterizing the binding interactions between MNPs and biomolecules in fluid volumes.

Homogeneous Differential Magnetic Assay.

Assays are widely used for detection of various targets, including pathogens, drugs, and toxins. Homogeneous assays are promising for the realization of point-of-care diagnostics as they do not require separation, immobilization, or washing steps. For low concentrations of target molecules, the speed and sensitivity of homogeneous assays have hitherto been limited by slow binding kinetics, time-consuming amplification steps, and the presence of a high background signal. Here, we present a homogeneous differential magnetic assay that utilizes a differential magnetic readout that eliminates previous limitations of homogeneous assays. The assay uses a gradiometer sensor configuration combined with precise microfluidic sample handling. This enables simultaneous differential measurement of a positive test sample containing a synthesized Vibrio cholerae target and a negative control sample, which reduces the background signal and increases the readout speed. Very low concentrations of targets down to femtomolar levels are thus detectable without any additional amplification of the number of targets. Our homogeneous differential magnetic assay method opens new possibilities for rapid and highly sensitive diagnostics at the point of care.

Spatio-temporal profile of brain activity during gentle touch investigated with magnetoencephalography.

Positive affective touch plays a central role in social and inter-personal interactions. Low-threshold mechanoreceptive afferents, including slowly-conducting C-tactile (CT) afferents found in hairy skin, transmit such signals from gentle touch to the brain. Tactile signals are processed, in part, by the posterior insula, where it is the thought to be the primary target for CTs. We used magnetoencephalography (MEG) to assess brain activity evoked by gentle, naturalistic stroking touch on the arm delivered by a new MEG-compatible brush robot. We aimed to use high temporal resolution MEG to allow us to distinguish between brain responses from fast-conducting Aß and slowly-conducting CT afferents. Brush strokes were delivered to the left upper arm and left forearm of 15 healthy participants. We hypothesized that late brain responses, due to slow CT afference, would appear with a time shift between the two different locations on the arm. Our results show that gentle touch rapidly activated somatosensory, motor, and cingulate regions within the first 100 ms of skin contact, which was driven by fast-conducting mechanoreceptive afference, and that these responses were sustained during touch. Peak latencies in the posterior insula were shifted as a function of stimulus location and temporally-separate posterior insula activations were induced by Aß and CT afference that may modulate the emotional processing of gentle touch on hairy skin. We conclude that the detailed information regarding temporal and spatial brain activity from MEG provides new insights into the central processing of gentle, naturalistic touch, which is thought to underpin affective tactile interactions.

Automatic brain lesion segmentation on standard MRIs of the human head: a scoping review protocol.

INTRODUCTION: Automatic brain lesion segmentation from medical images has great potential to support clinical decision making. Although numerous methods have been proposed, significant challenges must be addressed before they will become established in clinical and research practice. We aim to elucidate the state of the art, to provide a synopsis of competing approaches and identify contrasts between them. METHODS AND ANALYSIS: We present the background and study design of a scoping review for automatic brain lesion segmentation methods for conventional MRI according to the framework proposed by Arksey and O'Malley. We aim to identify common image processing steps as well as mathematical and computational theories implemented in these methods. We will aggregate the evidence on the efficacy and identify limitations of the approaches. Methods to be investigated work with standard MRI sequences from human patients examined for brain lesions, and are validated with quantitative measures against a trusted reference. PubMed, IEEE Xplore and Scopus will be searched using search phrases that will ensure an inclusive and unbiased overview. For matching records, titles and abstracts will be screened to ensure eligibility. Studies will be excluded if a full paper is not available or is not written in English, if non-standard MR sequences are used, if there is no quantitative validation, or if the method is not automatic. In the data charting phase, we will extract information about authors, publication details and study cohort. We expect to find information about preprocessing, segmentation and validation procedures. We will develop an analytical framework to collate, summarise and synthesise the data. ETHICS AND DISSEMINATION: Ethical approval for this study is not required since the information will be extracted from published studies. We will submit the review report to a peer-reviewed scientific journal and explore other venues for presenting the work.

Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines.

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.