Toward an optimal cadaveric brain model for neurosurgical education: assessment of preservation, parenchyma, vascular injection, and imaging.

OBJECTIVE: We assessed types of cadaveric head and brain tissue specimen preparations that are used in a high throughput neurosurgical research laboratory to determine optimal preparation methods for neurosurgical anatomical research, education, and training. METHODS: Cadaveric specimens (N = 112) prepared using different preservation and vascular injection methods were imaged, dissected, and graded by 11 neurosurgeons using a 21-point scale. We assessed the quality of tissue and preservation in both the anterior and posterior circulations. Tissue quality was evaluated using a 9-point magnetic resonance imaging (MRI) scale. RESULTS: Formalin-fixed specimens yielded the highest scores for assessment (mean ± SD [17.0 ± 2.8]) vs. formalin-flushed (17.0 ± 3.6) and MRI (6.9 ± 2.0). Cadaver assessment and MRI scores were positively correlated (P < 0.001, R2 0.60). Analysis showed significant associations between cadaver assessment scores and specific variables: nonformalin fixation (ß = -3.3), preservation within ≤72 h of death (ß = 1.8), and MRI quality score (ß = 0.7). Formalin-fixed specimens exhibited greater hardness than formalin-flushed and nonformalin-fixed specimens (P ≤ 0.006). Neurosurgeons preferred formalin-flushed specimens injected with colored latex. CONCLUSION: For better-quality specimens for neurosurgical education and training, formalin preservation within ≤72 h of death was preferable, as was injection with colored latex. Formalin-flushed specimens more closely resembled live brain parenchyma. Assessment scores were lower for preparation techniques performed > 72 h postmortem and for nonformalin preservation solutions. The positive correlation between cadaver assessment scores and our novel MRI score indicates that donation organizations and institutional buyers should incorporate MRI as a screening tool for the selection of high-quality specimens.

Neural substrates of the interaction between effort-expenditure reward decision-making and outcome anticipation.

OBJECTIVE: Reward anticipation is important for future decision-making, possibly due to re-evaluation of prior decisions. However, the exact relationship between reward anticipation and prior effort-expenditure decision-making, and its neural substrates are unknown. METHOD: Thirty-three healthy participants underwent fMRI scanning while performing the Effort-based Pleasure Experience Task (E-pet). Participants were required to make effort-expenditure decisions and anticipate the reward. RESULTS: We found that stronger anticipatory activation at the posterior cingulate cortex was correlated with slower reaction time while making decisions with a high-probability of reward. Moreover, the substantia nigra was significantly activated in the prior decision-making phase, and involved in reward-anticipation in view of its strengthened functional connectivity with the mammillary body and the putamen in trial conditions with a high probability of reward. CONCLUSIONS: These findings support the role of reward anticipation in re-evaluating decisions based on the brain-behaviour correlation. Moreover, the study revealed the neural interaction between reward anticipation and decision-making.

Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism.

Deuterium metabolic imaging (DMI) is an emerging magnetic resonance technique, for non-invasive mapping of human brain glucose metabolism following oral or intravenous administration of deuterium-labeled glucose. Regional differences in glucose metabolism can be observed in various brain pathologies, such as Alzheimer's disease, cancer, epilepsy or schizophrenia, but the achievable spatial resolution of conventional phase-encoded DMI methods is limited due to prolonged acquisition times rendering submilliliter isotropic spatial resolution for dynamic whole brain DMI not feasible. The purpose of this study was to implement non-Cartesian spatial-spectral sampling schemes for whole-brain 2H FID-MR Spectroscopic Imaging to assess time-resolved metabolic maps with sufficient spatial resolution to reliably detect metabolic differences between healthy gray and white matter regions. Results were compared with lower-resolution DMI maps, conventionally acquired within the same session. Six healthy volunteers (4 m/2 f) were scanned for ~90 min after administration of 0.8 g/kg oral [6,6']-2H glucose. Time-resolved whole brain 2H FID-DMI maps of glucose (Glc) and glutamate + glutamine (Glx) were acquired with 0.75 and 2 mL isotropic spatial resolution using density-weighted concentric ring trajectory (CRT) and conventional phase encoding (PE) readout, respectively, at 7 T. To minimize the effect of decreased signal-to-noise ratios associated with smaller voxels, low-rank denoising of the spatiotemporal data was performed during reconstruction. Sixty-three minutes after oral tracer uptake three-dimensional (3D) CRT-DMI maps featured 19% higher (p = .006) deuterium-labeled Glc concentrations in GM (1.98 ± 0.43 mM) compared with WM (1.66 ± 0.36 mM) dominated regions, across all volunteers. Similarly, 48% higher (p = .01) 2H-Glx concentrations were observed in GM (2.21 ± 0.44 mM) compared with WM (1.49 ± 0.20 mM). Low-resolution PE-DMI maps acquired 70 min after tracer uptake featured smaller regional differences between GM- and WM-dominated areas for 2H-Glc concentrations with 2.00 ± 0.35 mM and 1.71 ± 0.31 mM, respectively (+16%; p = .045), while no regional differences were observed for 2H-Glx concentrations. In this study, we successfully implemented 3D FID-MRSI with fast CRT encoding for dynamic whole-brain DMI at 7 T with 2.5-fold increased spatial resolution compared with conventional whole-brain phase encoded (PE) DMI to visualize regional metabolic differences. The faster metabolic activity represented by 48% higher Glx concentrations was observed in GM- compared with WM-dominated regions, which could not be reproduced using whole-brain DMI with the low spatial resolution protocol. Improved assessment of regional pathologic alterations using a fully non-invasive imaging method is of high clinical relevance and could push DMI one step toward clinical applications.

CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment.

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of 84.37 % .

Assessment of brain structure and volume reveals neurodevelopmental abnormalities in preterm infants with low-grade intraventricular hemorrhage.

There is increasing evidence of abnormal neurodevelopmental outcomes in preterm infants with low-grade intraventricular hemorrhage (IVH). The purpose of the study was to explore whether brain microstructure and volume are associated with neuro-behavioral outcomes at 40 weeks corrected gestational age in preterm infants with low-grade IVH. MR imaging at term-equivalent age (TEA) was performed in 25 preterm infants with mild IVH (Papile grading I/II) and 40 control subjects without IVH. These subjects all had neonatal behavioral neurological assessment (NBNA) at 40 weeks' corrected age. Microstructure and volume evaluation of the brain were performed by using diffusion kurtosis imaging (DKI) and Synthetic MRI. Correlations among microstructure parameters, volume, and developmental outcomes were explored by using Spearman's correlation. In preterm infants with low-grade IVH, the volume of brain parenchymal fraction (BPF) was reduced. In addition, mean kurtosis (MK), fractional anisotropy (FA), radial kurtosis (RK), axial kurtosis (AK) in several major brain regions were reduced, while mean diffusivity (MD) was increased (P < 0.05). BPF, RK in the cerebellum, MK in the genu of the corpus callosum, and MK in the thalamus of preterm infants with low-grade IVH were associated with lower NBNA scores (r = 0.831, 0.836, 0.728, 0.772, P < 0.05). DKI and Synthetic MRI can quantitatively evaluate the microstructure alterations and brain volumes in preterm infants with low-grade IVH, which provides clinicians with a more comprehensive and accurate neurobehavioral assessment of preterm infants with low-grade IVH.

Associations of dietary patterns and longitudinal brain-volume change in Japanese community-dwelling adults: results from the national institute for longevity sciences-longitudinal study of aging.

BACKGROUND: The association of dietary patterns and longitudinal changes in brain volume has rarely been investigated in Japanese individuals. We prospectively investigated this association in middle-aged and older Japanese community-dwelling adults. METHODS: Data with a 2-year follow-up from the sixth wave (July 2008 to July 2010; baseline) to the seventh (July 2010 to July 2012; follow-up) of the National Institute for Longevity Sciences-Longitudinal Study of Aging project were analyzed. Dietary intake was assessed using a 3-day dietary record, and longitudinal volume changes (%) in the total gray matter (TGM), total white matter, and frontal, parietal, occipital, temporal, and insular lobes were assessed using 3-dimensional T1 magnetic resonance imaging scans. Multiple factor analysis and hierarchical clustering revealed sex-specific dietary patterns. Associations between dietary patterns and annual brain-volume changes (%) were evaluated using general linear models adjusted for age, apoprotein E genotype, body mass index, medical history, lifestyle behaviors, socioeconomic factors, and energy intake. RESULTS: Among the 1636 participants (age: 40.3-89.2 years), three dietary patterns were determined for men (n = 815; Western; Vegetable-Fruit-Dairy; and Traditional Japanese diets) and women (n = 821; Western; Grain-Vegetable-Fruit; and Traditional Japanese diets). Compared to women following the Western diet, those on the Traditional Japanese diet had less TGM atrophy. Multivariable-adjusted ß (95% confidence interval) of the annual change (%) of TGM was - 0.145 (-0.287 to -0.002; P = 0.047), which correlated with reduced parietal lobe atrophy. No association between dietary pattern and brain atrophy was observed in men. CONCLUSIONS: Adherence to healthy dietary patterns, with higher consumption of whole grains, seafood, vegetables, fruits, mushrooms, soybean products, and green tea, potentially confers a protective effect against brain atrophy in middle-aged and older Japanese women but not in men. Further research to confirm these results and ascertain the underlying mechanisms is required. This study highlights the importance of sex-specific effects on the relationship between dietary patterns and brain health in diverse populations.

Neural correlates of the addictions neuroclinical assessment (ANA) incentive salience factor among individuals with alcohol use disorder.

The Addictions Neuroclinical Assessment (ANA) is a recently-developed framework offering a more holistic understanding of three neurofunctional and behavioral domains that reflect the neurobiological dysfunction seen in alcohol use disorder (AUD). While the ANA domains have been well-validated across independent laboratories, there is a critical need to identify neural markers that subserve the proposed neurofunctional domains. The current study involves secondary data analysis of a two-week experimental medication trial of ibudilast (50 mg BID). Forty-five non-treatment-seeking participants with AUD (17F / 28 M) completed a battery of validated behavioral assessments forming the basis of their incentive salience factor score, computed via factor analysis, as well as a functional neuroimaging (fMRI) task assessing their neural reactivity to visual alcohol cues after being on placebo or ibudilast for 7 days. General linear models were conducted to examine the relationship between incentive salience and neural alcohol cue-reactivity in the ventral and dorsal stratum. Whole-brain generalized linear model analyses were conducted to examine associations between neural alcohol cue-reactivity and incentive salience. Age, sex, medication, and smoking status were included as covariates. Incentive salience was not associated with cue-elicited activation in the dorsal or ventral striatum. Incentive salience was significantly positively correlated (p < 0.05) with alcohol cue-elicited brain activation in reward-learning and affective regions including the insula and posterior cingulate cortices, bilateral precuneus, and bilateral precentral gyri. The ANA incentive salience factor is reflected in brain circuitry important for reward learning and emotion processing. Identifying a sub-phenotype of AUD characterized by increased incentive salience to alcohol cues allows for precision medicine approaches, i.e. treatments specifically targeting craving and reward from alcohol use. This study serves as a preliminary bio-behavioral validation for the incentive salience factor of the ANA. Further studies validating the neural correlates of other ANA factors, as well as replication in larger samples, appear warranted.

Association of Age-Related Neuropathologic Findings at Autopsy With a Claims-Based Epilepsy Diagnosis in Older Adults.

BACKGROUND AND OBJECTIVES: Epilepsy is 1 of the 3 most common neurologic diseases of older adults, but few studies have examined its underlying pathologies in older age. We examined the associations of age-related brain pathologies with epilepsy in older persons. METHODS: Clinical and pathologic data came from 2 ongoing clinical pathologic cohort studies of community-dwelling older adults. Epilepsy was ascertained using Medicare fee-for-service Parts A and B claims data that were linked to data from the cohort studies. The postmortem pathologic assessment collected indices of 9 pathologies including Alzheimer disease, hippocampal sclerosis, macroinfarcts, and cerebral amyloid angiopathy. The fixed brain hemisphere was imaged using 3T MRI scanners before the pathologic assessments in a subgroup of participants. RESULTS: The participants (n = 1,369) were on average 89.3 (6.6) years at death, and 67.0% were women. Epilepsy was identified in 58 (4.2%) participants. Cerebral amyloid angiopathy (odds ratio [OR] = 2.21, 95% CI 1.24-3.95, p = 0.007) and cortical macroinfarcts (OR = 2.74, 95% CI 1.42-5.28, p = 0.003) were associated with a higher odds of epilepsy. Of note, hippocampal sclerosis and Alzheimer disease pathology were not associated with epilepsy (both p's > 0.25), although hippocampal sclerosis was not common and thus hard to examine with the modest number of epilepsy cases here. In 673 participants with MRI data, the association of cerebral amyloid angiopathy and cortical macroinfarcts with epilepsy did not change after controlling for cortical gray matter atrophy, which was independently associated with a higher odds of epilepsy (OR = 1.06, 95% CI 1.02-1.10, p = 0.003). By contrast, hippocampal volume was not associated with epilepsy. DISCUSSION: Cerebrovascular pathologies and cortical atrophy were associated with epilepsy in older persons.

Multivariate Assessment of Inhibitory Control in Youth: Links With Psychopathology and Brain Function.

Inhibitory control is central to many theories of cognitive and brain development, and impairments in inhibitory control are posited to underlie developmental psychopathology. In this study, we tested the possibility of shared versus unique associations between inhibitory control and three common symptom dimensions in youth psychopathology: attention-deficit/hyperactivity disorder (ADHD), anxiety, and irritability. We quantified inhibitory control using four different experimental tasks to estimate a latent variable in 246 youth (8-18 years old) with varying symptom types and levels. Participants were recruited from the Washington, D.C., metro region. Results of structural equation modeling integrating a bifactor model of psychopathology revealed that inhibitory control predicted a shared or general psychopathology dimension, but not ADHD-specific, anxiety-specific, or irritability-specific dimensions. Inhibitory control also showed a significant, selective association with global efficiency in a frontoparietal control network delineated during resting-state functional magnetic resonance imaging. These results support performance-based inhibitory control linked to resting-state brain function as an important predictor of comorbidity in youth psychopathology.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.

Background: Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke. Objectives: To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting. Methods: Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care. Results: A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher. Limitations and conclusions: The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective. Future work: Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice. Study registration: This study is registered as PROSPERO CRD42021269609. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.

Stroke is a serious life-threatening medical condition caused by a blood clot or haemorrhage in the brain. Quick and effective management, including a brain scan, of the patients with suspected stroke can make a big difference in their outcome. Artificial intelligence-derived computer programmes exist that are intended to help with the interpretation of computed tomography scans of the brain in stroke. We undertook a thorough review of the existing research into the effectiveness and value for money of using these programmes to help doctors and other specialists to interpret computed tomography brain scans. We found very little evidence to tell us how well artificial intelligence-derived computer programmes work in practice. Some studies have looked at artificial intelligence-derived computer programmes on their own (i.e. not taken together with a doctor's judgement, as they were designed to be used). Other studies have looked at what happens to patients who are treated for stroke when artificial intelligence-derived computer programmes are used; these studies provide no information about whether using artificial intelligence-derived computer programmes may have led to patients who could have benefitted from treatment being missed. It is unclear how well artificial intelligence-derived software-assisted review works when added to current clinical practice.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data.

BACKGROUND: For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. NEW METHOD: We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. RESULTS: Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. COMPARISON WITH EXISTING METHOD(S): Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. CONCLUSIONS: As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.

The neurophysiological consequences of racism-related stressors in Black Americans.

Racism-related stressors, from experiences of both implicit and explicit racial discrimination to systemic socioeconomic disadvantage, have a cumulative impact on Black Americans' health. The present narrative review synthesizes peripheral (neuroendocrine and inflammation markers), psychophysiological (heart-rate variability, skin conductance), and neuroimaging (structural and functional) findings that demonstrate unique associations with racism-related stress. Emerging evidence reveals how racism-related stressors contribute to differential physiological and neural responses and may have distinct impacts on regions involved with threat and social processing. Ultimately, the neurophysiological effects of racism-related stress may confer biological susceptibility to stress and trauma-related disorders. We note critical gaps in the literature on the neurophysiological impact of racism-related stress and outline additional research that is needed on the multifactorial interactions between racism and mental health. A clearer understanding of the interactions between racism-related stress, neurophysiology, and stress- and trauma-related disorders is critical for preventative efforts, biomarker discovery, and selection of effective clinical treatments for Black Americans.

Assessment of Perivascular Space Morphometry Across the White Matter in Huntington's Disease Using MRI.

Background: Perivascular spaces (PVS) are fluid-filled cavities surrounding small cerebral blood vessels. There are limited reports of enlarged PVS across the grey matter in manifest Huntington's disease (HD). Little is known about how PVS morphometry in the white matter may contribute to HD. Enlarged PVS have the potential to both contribute to HD pathology and affect the distribution and success of intraparenchymal and intrathecally administered huntingtin-lowering therapies. Objective: To investigate PVS morphometry in the global white matter across the spectrum of HD. Relationships between PVS morphometry and disease burden and severity measures were examined. Methods: White matter PVS were segmented on 3T T2 W MRI brain scans of 33 healthy controls, 30 premanifest HD (pre-HD), and 32 early manifest HD (early-HD) participants from the Vancouver site of the TRACK-HD study. PVS count and total PVS volume were measured. Results: PVS total count slightly increased in pre-HD (p = 0.004), and early-HD groups (p = 0.005), compared to healthy controls. PVS volume, as a percentage of white matter volume, increased subtly in pre-HD compared to healthy controls (p = 0.044), but not in early-HD. No associations between PVS measures and HD disease burden or severity were found. Conclusions: This study reveals relatively preserved PVS morphometry across the global white matter of pre-HD and early-HD. Subtle morphometric abnormalities are implied but require confirmation in a larger cohort. However, in conjunction with previous publications, further investigation of PVS in HD and its potential impact on future treatments, with a focus on subcortical grey matter, is warranted.

Shear shock wave injury in vivo: High frame-rate ultrasound observation and histological assessment.

Using high frame-rate ultrasound and ¡1µm sensitive motion tracking we previously showed that shear waves at the surface of ex vivo and in situ brains develop into shear shock waves deep inside the brain, with destructive local accelerations. However post-mortem tissue cannot develop injuries and has different viscoelastodynamic behavior from in vivo tissue. Here we present the ultrasonic measurement of the high-rate shear shock biomechanics in the in vivo porcine brain, and histological assessment of the resulting axonal pathology. A new biomechanical model of brain injury was developed consisting of a perforated mylar surface attached to the brain and vibrated using an electromechanical shaker. Using a custom sequence with 8 interleaved wide beam emissions, brain imaging and motion tracking were performed at 2900 images/s. Shear shock waves were observed for the first time in vivo wherein the shock acceleration was measured to be 2.6 times larger than the surface acceleration ( 95g vs. 36g). Histopathology showed axonal damage in the impacted side of the brain from the brain surface, accompanied by a local shock-front acceleration of >70g. This shows that axonal injury occurs deep in the brain even though the shear excitation was at the brain surface, and the acceleration measurements support the hypothesis that shear shock waves are responsible for deep traumatic brain injuries.

An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models.

BACKGROUND: The dynamic and hierarchical nature of the functional brain network. The neural dynamical systems tend to converge to multiple attractors (stable fixed points or dynamical states) in long run. Little is known about how the changes in this brain dynamic "long-term" behavior of the connectivity flow of brain network in generalized anxiety disorder (GAD). METHODS: This study recruited 92 patients with GAD and 77 healthy controls (HC). We applied a reachable probability approach combining a Non-homogeneous Markov model with transition probability to quantify all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level and the stationary probability vector (10-step transition probabilities) to describe the steady state of the system in the long run. A random forest algorithm was conducted to predict the severity of anxiety. RESULTS: The dynamic functional patterns in distributed brain networks had larger possibility to converge in bilateral thalamus, posterior cingulate cortex (PCC), right superior occipital gyrus (SOG) and smaller possibility to converge in bilateral superior temporal gyrus (STG) and right parahippocampal gyrus (PHG) in patients with GAD compared to HC. The abnormal transition probability pattern could predict anxiety severity in patients with GAD. LIMITATIONS: Small samples and subjects taking medications may have influenced our results. Future studies are expected to rule out the potential confounding effects. CONCLUSION: Our results have revealed abnormal dynamic neural communication and integration in emotion regulation in patients with GAD, which give new insights to understand the dynamics of brain function of patients with GAD.

Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.

OBJECTIVES: This study aimed to investigate the temporal dynamics of brain activity and characterize the spatiotemporal specificity of transitions and large-scale networks on short timescales in acute mild traumatic brain injury (mTBI) patients and those with cognitive impairment in detail. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 71 acute mTBI patients and 57 age-, sex-, and education-matched healthy controls (HCs). A hidden Markov model (HMM) analysis of rs-fMRI data was conducted to identify brain states that recurred over time and to assess the dynamic patterns of activation states that characterized acute mTBI patients and those with cognitive impairment. The dynamic parameters (fractional occupancy, lifetime, interval time, switching rate, and probability) between groups and their correlation with cognitive performance were analyzed. RESULTS: Twelve HMM states were identified in this study. Compared with HCs, acute mTBI patients and those with cognitive impairment exhibited distinct changes in dynamics, including fractional occupancy, lifetime, and interval time. Furthermore, the switching rate and probability across HMM states were significantly different between acute mTBI patients and patients with cognitive impairment (all p < 0.05). The temporal reconfiguration of states in acute mTBI patients and those with cognitive impairment was associated with several brain networks (including the high-order cognition network [DMN], subcortical network [SUB], and sensory and motor network [SMN]). CONCLUSIONS: Hidden Markov models provide additional information on the dynamic activity of brain networks in patients with acute mTBI and those with cognitive impairment. Our results suggest that brain network dynamics determined by the HMM could reinforce the understanding of the neuropathological mechanisms of acute mTBI patients and those with cognitive impairment.

Predictive Value of Fidgety Movement Assessment and Magnetic Resonance Imaging for Cerebral Palsy in Infants.

BACKGROUND: The early prediction of cerebral palsy (CP) could enable the follow-up of high-risk infants during the neuroplasticity period. This study aimed to explore the predictive value of fidgety movement assessment (FMA) and brain magnetic resonance imaging (MRI) for the development of CP in clinic rehabilitation setting. METHODS: This retrospective observational study included infants who underwent FMA and brain MRI at age nine to 20 weeks at Children's Hospital, Zhejiang University School of Medicine, between March 2018 and September 2019. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of FMA and MRI for predicting the development of CP were assessed. RESULTS: A total of 258 infants (169 males, gestational age 37.4 ± 3.0 weeks, birth weight 2987.9 ± 757.1 g) were included. Fifteen children had CP after age two years. The diagnostic value of FMA and brain MRI combination showed 86.7% sensitivity (95% confidence interval [CI]: 58.4% to 97.7%), 98.4% specificity (95% CI: 95.6% to 99.5%), and 97.7% accuracy (95% CI: 95.0% to 99.1%); the combination diagnostic value also showed a significantly higher AUC for predicting CP after age two years than FMA alone (AUC: 0.981 vs 0.893, P = 0.013). CONCLUSIONS: The diagnostic value of FMA and brain MRI combination during infancy showed a high predictive value for CP development in clinical rehabilitation setting.