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Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM
Anatomical magnetic resonance imaging (MRI) templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the Oxford-MultiModal-1 (OMM-1) template and age-dependent templates for each year of life between 45 and 81 years. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1-weighted (T1), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and diffusion tensor imaging (DTI) data. The OMM-1 template was constructed with a multiresolution, iterative approach using 240 individuals in the 50–55-year age range. The age-dependent templates were estimated using a Gaussian process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size are not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.
Stacking models of brain dynamics to improve prediction of subject traits in fMRI.
Beyond structural and time-averaged functional connectivity brain measures, modelling the way brain activity dynamically unfolds can add important information to our understanding and characterisation of individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, these predictions are susceptible to variability due to factors such as variation in model estimation induced by the choice of hyperparameters. We suggest that, rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. To leverage this variability, we propose the use of stacking, a prediction-driven approach for model selection. Specifically, we combine predictions developed from multiple hidden Markov models-a probabilistic generative model of network dynamics that identifies recurring patterns of brain activity-to demonstrate that stacking can slightly improve the accuracy and robustness of cognitive trait predictions. By comparing analysis from the Human Connectome Project and UK Biobank datasets, we show that stacking is relatively effective at improving prediction accuracy and robustness when there are enough subjects, and that the effectiveness of combining predictions from static and dynamic functional connectivity approaches depends on the length of scan per subject. We also show that the effectiveness of stacking predictions is driven by the accuracy and diversity in the underlying model estimations.
Neural correlates of cognitive ability and visuo-motor speed: Validation of IDoCT on UK Biobank Data
Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability, and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuo-motor speeds, testing device latencies, and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuo-motor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuo-motor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuo-motor versus language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities, and brain-wide associations.
Automated Assessment of Pain (AAP) and Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4Pain)
Pain communication varies, with some individuals being highly expressive regarding their pain and others exhibiting stoic forbearance and minimal verbal account of discomfort. Considerable progress has been made in defining behavioral indices of pain [1]-[3]. An abundant literature shows that a limited subset of facial movements, in several non-human species, encode pain intensity across the lifespan [2]. To advance reliable pain monitoring, automated assessment of pain is emerging as a powerful mean to realize that goal. Though progress has been made, this field remains in its infancy. The workshop aims to promote current research and support growth of interdisciplinary collaborations to advance this groundbreaking research.
The AI4Pain Grand Challenge 2024: Advancing Pain Assessment with Multimodal fNIRS and Facial Video Analysis
The Multimodal Sensing Grand Challenge for NextGen Pain Assessment (AI4PAIN) is the first international competition focused on automating the recognition of acute pain using multimodal sensing technologies. Participants are tasked with classifying pain intensity into three categories: No Pain, Low Pain, and High Pain, utilising functional near-infrared spectroscopy (fNIRS) and facial video recordings. This paper presents the baseline results of our approach, examining both individual and combined modalities. Notably, this challenge represents a pioneering effort to advance pain recognition by integrating neurological information (fNIRS) with behavioural data (facial video). The AI4Pain Grand Challenge aims to generate a novel multimodal sensing dataset, facilitating benchmarking and serving as a valuable resource for future research in autonomous pain assessment. The results show that individual fNIRS data achieved the highest accuracy, with 43.2% for the validation set and 43.3% for the test set, while facial data yielded the lowest accuracy, with 40.0% for the validation set and 40.1% for the test set. The combined multimodal approach produced accuracies of 40.2% for the validation set and 41.7% for the test set. This challenge provides the research community with a significant opportunity to enhance the understanding of pain, ultimately aiming to improve the quality of life for many pain sufferers through advanced, automated pain assessment methods.
Steady-state free precession for T2* relaxometry: All echoes in every readout with k-space aliasing.
PURPOSE: Multi-echo gradient echo imaging is useful for a range of applications including relaxometry, susceptibility mapping, and quantifying relative proportions of fat and water. This relies primarily on long-TR multi-echo gradient echo sequences (FLASH), which by design isolate one signal component (i.e., echo) at a time per readout. In this work, we propose an alternative strategy that simultaneously measures all signal components at once in every readout event with an N-periodic SSFP sequence. Essentially, we Fourier encode the signals into an "F-k space" similar to the "TE-k space" of a multi-echo gradient echo acquisition. This enables an efficient, short-TR relaxometry experiment where signals benefit from averaging effects over multiple excitations. THEORY AND METHODS: In the presented approach, multiple echoes are recorded simultaneously and separated by their differing phase evolution over multiple TRs. At low flip angles the relative echo amplitudes and phases are equivalent to those acquired sequentially from a multi-echo FLASH, in terms of both T2* weighting and spatial phase distributions. The two approaches were compared for the example of R2* relaxometry in a phantom and in human volunteers. RESULTS: The proposed approach shows close agreement in R2* estimation with multi-echo FLASH, with the advantage of more rapid temporal sampling. CONCLUSION: The proposed approach is a promising alternative to other relaxometry approaches, by measuring signals from multiple echo pathways simultaneously and separating them based on a simple analytical model.
Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates.
INTRODUCTION: Use of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data. METHODS: We correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction. RESULTS: Applying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates. CONCLUSIONS: Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.
Analysing the effect of early acetazolamide administration on patients with a high risk of permanent cerebrospinal fluid leakage.
In this study, we examined the role of early acetazolamide administration in reducing the risk of cerebrospinal fluid (CSF) leakage in patients with a high risk of permanent CSF leakage. In a randomised clinical trial, 57 patients with a high risk of permanent CSF leakage (rhinorrhea, otorrhea, pneumatocele or imaging-based evidence of severe skull-base fracture) were analysed. In the experimental group, acetazolamide, at 25 mg/kg/day, was started in the first 48 hours after admission. In the control group, acetazolamide was administered after the first 48 hours at the same dose administered to the patients in the experimental group. The following factors were compared between the two groups: duration of CSF leakage, duration of hospital stay, incidence of meningitis, need for surgical intervention and need for lumbar puncture (LP) and lumbar drainage (LD). All of the patients in the experimental group stopped having CSF leakage less than 14 days after the first day of admission, but 6 out of 21 patients (22%) in the control group continued having CSF leakage after 14 days of admission, which was a significant difference (P=0.01). This study showed that early acetazolamide administration can prevent CSF leakage in patients with a high risk of permanent CSF leak.
A 12-year epidemiologic study on primary spinal cord tumors in Isfahan, Iran.
BACKGROUND: Although primary spinal cord tumors (PSCTs) comprise a minority of primary central nervous system tumors, they often impose a great deal of morbidity on their victims. Few epidemiologic studies have addressed PSCTs in Iran. MATERIALS AND METHODS: We analyzed the demographic/clinical features of all primary intraspinal tumors (with a specific focus on primary intradural spinal cord tumors) identified between 1992 and 2004 in three of the major related hospitals in Isfahan, Iran. We also tracked the malignant cases until 2012. RESULTS: 102 patients with primary intraspinal tumors were found; 82 tumors were Intradural (36 intramedullary and 46 extramedullary) and 20 extradural. The principal intradural histological subtypes were nerve sheath tumor (33%), ependymoma (22%), astrocytoma (16%), and meningioma (15%). 20 (19%) of the tumors were malignant. Local pain (43%) and motor disabilities (36%) were the most common first-presenting symptoms in the patients. Male-to-female ratio was significant only in ependymoma (male:female ratio = 3.6, P < 0.05). The mean age in meningioma (57 years, standard error [SE]: 15.7) was significantly higher than other types (one-way ANOVA, P < 0.05). CONCLUSION: Our results reflect analogous frequency of distribution for PSCTs compared with most of the previous counterpart studies worldwide. The only notable exception was the comparatively fewer frequency of spinal cord meningioma in our study.
Neurotransmitter modulation of human facial emotion recognition
Human facial emotion recognition (FER) is an evolutionarily preserved process that influences affiliative behaviours, approach/avoidance and fight-or-flight responses in the face of detecting threat cues, thus enhancing adaptation and survival in social groups. Here, we provide a narrative literature review on how human FER is modulated by neurotransmitters and pharmacological agents, classifying the documented effects by central neurotransmitter systems. Synthesising the findings from studies involving functional neuroimaging and FER tasks, we highlight several emerging themes; for example, noradrenaline promotes an overall positive bias in FER, while serotonin, dopamine and gamma-aminobutyric acid modulate emotions relating to self-preservation. Finally, other neurotransmitters including the cholinergic and glutamatergic systems are responsible for rather non-specific pro-cognitive effects in FER. With the ongoing accumulation of evidence further characterising the individual contributions of each neurotransmitter system, we argue that a sensible next step would be the integration of experimental neuropharmacology with computational models to infer further insights into the temporal dynamics of different neurotransmitter systems modulating FER.
SNR‐efficient whole‐brain pseudo‐continuous arterial spin labeling perfusion imaging at 7 T
AbstractPurposeTo optimize pseudo‐continuous arterial spin labeling (PCASL) parameters to maximize SNR efficiency for RF power constrained whole brain perfusion imaging at 7 T.MethodsWe used Bloch simulations of pulsatile laminar flow to optimize the PCASL parameters for maximum SNR efficiency, balancing labeling efficiency and total RF power. The optimization included adjusting the inter‐RF pulse spacing (TRPCASL), mean B1+ (B1+ave), slice‐selective gradient amplitude (Gmax), and mean gradient amplitude (Gave). In vivo data were acquired from six volunteers at 7 T to validate the optimized parameters. Dynamic B0‐shimming and flip angle adjustments were used to avoid needing to make the PCASL parameters robust to B0/B1+ variations.ResultsThe optimized PCASL parameters achieved a significant (3.3×) reduction in RF power while maintaining high labeling efficiency. This allowed for longer label durations and minimized deadtime, resulting in a 118% improvement in SNR efficiency in vivo compared to a previously proposed protocol. Additionally, the static tissue response was improved, reducing the required distance between labeling plane and imaging volume.ConclusionThese optimized PCASL parameters provide a robust and efficient approach for whole brain perfusion imaging at 7 T, with significant improvements in SNR efficiency and reduced specific absorption rate burden.
Hypothalamic volume, sleep, and APOE genotype in cognitively healthy adults
INTRODUCTION: Sleep dysfunction in those at higher risk of dementia may be associated with early structural changes to the hypothalamus. METHODS: We used multivariate regression to analyze self-reported sleep (Pittsburgh Sleep Quality Index [PSQI]) from cognitively healthy participants in the PREVENT Dementia and Alzheimer's and Families (ALFA) studies (n = 1939), stratified by apolipoprotein E (APOE) genotype as homozygotes, heterozygotes, and non-carriers. FreeSurfer was used to extract hypothalamic subunit volumes from T1-weighted magnetic resonance images. RESULTS: APOE ε4 homozygotes had a larger anterior–superior hypothalamus compared to heterozygotes and non-carriers, an effect which was driven by younger people in the cohort. APOE ε4 carriers had a higher PSQI global score after age 55, and smaller anterior–superior and tubular–superior subunits were associated with more sleep disturbances. Sleep duration and efficiency worsened with age, but only in participants with a small anterior–inferior hypothalamus. DISCUSSION: This suggests that aging and APOE ε4 are associated with hypothalamic changes, highlighting mechanisms linking sleep dysfunction to dementia. Highlights: Apolipoprotein E (APOE) ε4 homozygotes ha a larger anterior–superior hypothalamus. APOE ε4 carriers have worse sleep, but only after age 55. Worse sleep in APOE ε4 carriers was associated with smaller hypothalamic subunits. Higher age was associated with worse sleep in people with a small hypothalamus.
Pain in women: bridging the gender pain gap.
Bridging the gender pain gap requires collaborative efforts that address female-specific biological and psychosocial dimensions of pain through evidence-based, compassionate and empathy-driven approaches.