Average Template Research Articles

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Diagnosing Maxillary Occlusal Cant with average 3D Skeletofacial Model Template.

Diagnosing maxillary occlusal cant involves varied methods, yet classification remains incomplete. This includes determining if the cant results from equal faults on both sides or if one side exhibits overdevelopment while the other underdevelops. This study introduces an innovative average 3D skull template for classifying maxillary occlusal cant instances. Sixty adult patients were divided into two groups: 30 with a mesoprosopic facial type and 30 with a leptoprosopic facial type. Using seven pairs of anatomical landmarks (N, Or (L, R), Lo (L, R), Zy (L, R)), the average skull models were aligned and superimposed onto the patients' skull models. Image resizing facilitated optimal registration area fitting. Vertical distances from the U6 mesial buccal cusp tip to the FH plane were measured to analyze occlusal cant, classified into three classes: overdevelopment (Class A), underdevelopment (Class B) and equal fault on both sides (Class C). In the mesoprosopic group, Class B t was most common (40%), followed by Class A (27%) and Class C (33%). In the leptoprosopic facial type group, Class B remained prevalent (50%), followed by Class A (43%) and Class C (7%). Statistical analysis revealed a significant association between facial types and the three classes. Contrary to expectations, equal fault patterns were not predominant; instead, underdevelopment of the maxilla constituted the majority of occlusal cant cases. Utilizing average 3D skeletofacial models as templates emerges as an effective tool and alternative for diagnosing the instance of maxillary occlusal cant. Therapeutic, II.

A Diffusion Tensor Imaging White Matter Atlas of the Domestic Canine Brain

Abstract There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts—including association, projection, commissural, brainstem, olfactory and cerebellar tracts—were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.

Correlation between matrix metalloproteinase-2, matrix metalloproteinase-9, and tissue inhibitor of metalloproteinases-1 and white matter hyperintensities in patients with cerebral small vessel disease based on cranial magnetic resonance 3D imaging

ObjectiveThe objective of this study was to investigate the correlation between serum levels of matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), and tissue inhibitor of metalloproteinases-1 (TIMP-1) levels and their ratios with the severity of white matter hyperintensities (WMHs) in patients with cerebral small vessel disease (CSVD). MethodsThis cross-sectional study was done on a prospective cohort of patients with CSVD. Qualitative and quantitative analyses of WMHs were performed using Fazekas grading and lesion prediction algorithm (LPA) methods. Biomarkers MMP-2, MMP-9, and TIMP-1 were measured to explore their correlation with the severity of WMHs. ResultsThe sample consisted of 144 patients with CSVD. There were 63 male and 81 female patients, with an average age of 67.604 ± 8.727 years. Among these, 58.33% presented with white matter hyperintensities at Fazekas grading level 1, with an average total template volume of WMHs of 4.305 mL. MMP-2 (P = 0.025), MMP-9 (P = 0.008), TIMP-1 (P = 0.026), and age (P = 0.007) were identified as independent correlates of WMHs based on Fazekas grading. Independent correlates of the total template volume of WMHs included MMP-2 (P = 0.023), TIMP-1 (P = 0.046), age (P = 0.047), systolic blood pressure (P = 0.047), and homocysteine (Hcy) (P = 0.014). In addition, age (P = 0.003; P < 0.001), interleukin-6 (IL-6) (P < 0.001; P = 0.044), Hcy (P < 0.001; P < 0.001), glycated hemoglobin (HbA1c) (P = 0.016; P = 0.043), and chronic kidney disease (P < 0.001; P < 0.001) were associated with both WMHs Fazekas grading and the total template volume of WMHs. ConclusionSerum levels of MMP-9, MMP-2, and TIMP-1 were independently associated with the Fazekas grading, while serum TIMP-1 and MMP-2 levels were independently related to the total template volume of WMHs. The association of TIMP-1 and MMP-2 with the severity of CSVD-related WMHs suggests their potential role as disease-related biomarkers. However, further research is required to uncover the specific mechanisms underlying these interactions.

P D N: A Priori Dictionary Network for Fashion Parsing

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets.

What does the mean mean? A simple test for neuroscience.

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

148 Ventral Mammillo-tegmental Tract Atrophies With Normal Aging

INTRODUCTION: We recently characterized the Ventral Mammillo-tegmental tract (VMTT) throug Klingler’s microdissections and fiber tractography using an averaged template generated from diffusion MRI (dMRI) data of 1065 human healthy subjects acquired from the Human Connectome Project (HCP) and the 50/200 μm cadaveric brainstem dMRI Duke database. The VMTT interconnects the ventral tegmental area with mammillary bodies, regions that are profoundly affected in dementia. We hypothesize that his tract is involved in memory which is inherently affected by normal brain aging and thus atrophy over time. METHODS: We performed fiber tractography of the VMTT and the corticospinal tract (CST), using individual subject data from multiple HCP databases spanning neonatal, developmental, healthy adult, and aging populations. These data were generated using an averaged template from diffusion MRI (dMRI) data of 1065 healthy human subjects acquired from the Human Connectome Project (HCP) and the Duke database of cadaveric brainstem dMRI. The CST served as a control in this study. Tract diameter, volume, and fractional anisotropy were measured using DSI Studio developed by FCY. Trends analysis was performed evaluating diameter changes by age. RESULTS: The tract was reconstructed in total of 20 subjects for ages ranging from 9 to 100 years old. Our analysis revealed a significant age-related atrophy of the VMTT (p &lt; 0.0001) while no age-related atrophy was observed in the CST (p = 0.161). CONCLUSIONS: Our findings indicate that the VMTT experiences disproportionate atrophy with normal aging as compared to CST which served as a control tract. This highlights the distinct age-related changes in the VMTT. Future dMRI studies on this tract across diverse patient populations will shed light on its potential role as a biomarker for early diagnosis of dementia disorders and its utility as a deep brain stimulation (DBS) target.

The non-decussating and decussating trigeminothalamic tracts in humans: A combination of connectome-based tractography and histological validation.

Functional anatomical research proposed the existence of a bilateral trigeminal ascending system although the anatomy trajectories of the trigeminothalamic connections cranial to the pons remain largely elusive. This study therefore aimed to clarify the anatomical distributions of the trigeminothalamic connections in humans. Advanced deterministic tractography to an averaged template of diffusion tensor imaging data from 1065 subjects from the Human Connectome Project was used. Seedings masks were placed in Montreal Neurological Institute standard space by use of the BigBrain histological dataset. Waypoint masks of the sensory thalamus was obtained from the Brainnetome Atlas. Tractography results were validated by use of the BigBrain histological dataset and Polarized Light Imaging microscopy. The trigeminothalamic tract bifurcated into a decussating ventral and a non-decussating dorsal tract. The ventral and dorsal tracts ascended to the contralateral thalamus and ipsilateral thalamus and reflected the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract, respectively. The projection of the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract to both thalami confirm the existence of a bilateral trigeminothalamic system in humans. Because our study is strictly anatomical, no further conclusions can be drawn with regard to physiological functionality. Future research should explore if the dorsal trigeminothalamic tract and the ventral trigeminothalamic tract actually transmit signals from noxious stimuli, this offers potential in understanding and possibly treating neuropathology in the orofacial region.

Precise tooth design using deep learning-based templates

ObjectivesIn prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration. MethodsNinety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences. ResultsDIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT. ConclusionThe proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration. Clinical significanceThis DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.

Neuroanatomical mapping of inhibitory attention and working memory with functional magnetic resonance imaging in healthy children

The objective is to produce an average brain activation mapping template in healthy children using functional magnetic resonance imaging (fMRI), with specific paradigms for activating inhibitory attention and working memory functions. A nutritional and neuropsychological evaluation was performed on 87 right-handed children. The inclusion criteria were met by 30 children (15 boys and 15 girls) between 9 and 11 years old, who were studied with fMRI in two inhibitory attention tests (Go/No Go), with food cues, a working memory test (Continuous Performance Test Identical Pairs) and measurement of anatomical volumes. These data were subsequently processed with the FSL-v5 program, with a threshold of p < 0.05 (cluster-wise). The brain areas activated were located using a standard Montreal Neurological Institute brain template and the Harvard-Oxford structural cortical atlas. The inhibitory attention tests showed activation frontal areas predominantly on the right, and the cingulate, parietal and occipital areas, with preponderance in occipital areas in the food cues test. In the Continuous Performance Test-Identical Pairs test, activation was obtained predominantly in the occipital, frontal and parietal areas. Brain activity mapping templates are obtained in healthy children with tests for inhibitory attention, food cues and working memory. The activation areas are mostly those reported in the literature. This provides baseline brain activation patterns for studying pathologies related to inhibitory attention, impulsivity and working memory.

Optimizing Orthognathic Surgery: Leveraging the Average Skull as a Dynamic Template for Surgical Simulation and Planning in 30 Patient Cases.

Virtual planning has revolutionized orthognathic surgery (OGS), marking a significant advancement in the field. This study aims to showcase the practical application of our established 3D average skull template as a guiding framework for surgical planning, and to share valuable insights from our clinical experience. We enrolled 30 consecutive Taiwanese patients (18 females and 12 males) who underwent two-jaw orthognathic surgery with surgical simulation, utilizing the average skull template for planning. Results indicate the method's applicability and precision. By adhering to the surgical plan, post-operative outcomes closely aligned with the average skull template, showing negligible deviations of less than 2 mm. Moreover, patients expressed high satisfaction with post-surgery facial changes, with the chin appearance receiving the highest satisfaction scores, while the lowest scores were attributed to nose appearance. Notably, the substantial change in lower jaw position post-mandibular setback surgery contributed to increased satisfaction with the chin position. In conclusion, this study does not seek to replace established surgical planning methods, but underscores that utilizing an average skull as a surgical design template provides a viable, accurate, and efficient option for OGS patients.

WarpDrive: Improving spatial normalization using manual refinements

Spatial normalization—the process of mapping subject brain images to an average template brain—has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

The anatomy of the four streams of the prefrontal cortex. Preliminary evidence from a population based high definition tractography study.

The model of the four streams of the prefrontal cortex proposes 4 streams of information: motor through Brodmann area (BA) 8, emotion through BA 9, memory through BA 10, and emotional-related sensory through BA 11. Although there is a surge of functional data supporting these 4 streams within the PFC, the structural connectivity underlying these neural networks has not been fully clarified. Here we perform population-based high-definition tractography using an averaged template generated from data of 1,065 human healthy subjects acquired from the Human Connectome Project to further elucidate the structural organization of these regions. We report the structural connectivity of BA 8 with BA 6, BA 9 with the insula, BA 10 with the hippocampus, BA 11 with the temporal pole, and BA 11 with the amygdala. The 4 streams of the prefrontal cortex are subserved by a structural neural network encompassing fibers of the anterior part of the superior longitudinal fasciculus-I and II, corona radiata, cingulum, frontal aslant tract, and uncinate fasciculus. The identified neural network of the four streams of the PFC will allow the comprehensive analysis of these networks in normal and pathological brain function.

A method framework of automatic localization and quantitative segmentation for the cavum septum pellucidum complex and the cerebellar vermis in fetal brain ultrasound images.

Early detection of central nervous system (CNS) anomalies in human embryos through prenatal screening is crucial for timely intervention and improved patient outcomes. Fetal brain mid-sagittal ultrasound images (FBMUIs) play a pivotal role as a diagnostic tool for detecting structural abnormalities. However, the automatic localization and quantitative segmentation of complex anatomical structures such as the corpus callosum-cavum septum pellucidum complex (CCC) and cerebellar vermis (CV) in FBMUIs present significant challenges. To address this issue, we propose an integrated framework that combines anatomical knowledge with computer vision techniques. Our framework comprises four steps: (I) generation of average templates for CCC and CV local images using a variational autoencoder (VAE); (II) localizing the CCC by using the "Initial Localization-Accurate Localization-Result Detection" strategy, followed by segmenting it based on morphological characteristics using the "Initial Contour Fitting-Contour Iteration" strategy; (III) applying a similar strategy as CCC localization and CV segmentation; and (IV) leveraging spatial and morphological characteristics to achieve accurate localization and segmentation. Our CCC and CV localization and segmentation methods were validated by using 140 FBMUIs from various perspectives. The accuracy and effectiveness of our approach were demonstrated through data statistics and comparative analysis. Currently, clinical trials are being conducted on our method at Shengjing Hospital of China Medical University. Our proposed integrated framework presents a novel solution for the automatic localization and quantitative segmentation of the CCC and CV in FBMUIs. It shows promise for early diagnosis of CNS anomalies in human embryos, offering significant clinical implications.

Statistical shape modeling-based algorithm for replacing missing beats in blood pressure signals.

In clinical environments, such as the Intensive Care Unit (ICU), continuous and uninterrupted monitoring of vital signs is critical for the early detection of patient deterioration and prompt intervention. Since data collected in these settings are often corrupted by noise, artifacts, or recording gaps, it is important to estimate missing data for a more accurate signal assessment.In this study, we propose an automatic algorithm for reconstructing of arterial blood pressure signal waveforms. The methodological core of the algorithm is based on the idea of statistical shape modeling, which basically estimates the shape variation of beat waveforms in order to reconstruct them in noisy segments. The waveform reconstruction is achieved by combining the average beat template from a 90-second segment of clean signal preceding the gap with the main shape variations of the estimated waveform.The algorithm was validated using arterial blood pressure recordings from 9 subjects admitted in the ICU and collected in the MIMIC-III Waveform Database, each lasting 1 hour and sampled at 125 Hz. For each record, ten fictitious gaps were created, and the reconstructed segments were compared to the original signals with the metrics proposed within the PhysioNet / Computing in Cardiology Challenge 2010. Results demonstrate the excellent performance of the proposed algorithm, with overall averages of both Q1 and Q2 metrics greater than 0.85.

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the “gold standard” approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4–23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.

Neuroimaging-based analysis of DBS outcomes in pediatric dystonia: Insights from the GEPESTIM registry

IntroductionDeep brain stimulation (DBS) is an established treatment in patients of various ages with pharmaco-resistant neurological disorders. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures, and on electrode connectivity to a specific distribution pattern within brain networks. Such information is usually collected using group-level analysis, which relies on the availability of normative imaging resources (atlases and connectomes). Analysis of DBS data in children with debilitating neurological disorders such as dystonia would benefit from such resources, especially given the developmental differences in neuroimaging data between adults and children. We assembled pediatric normative neuroimaging resources from open-access datasets in order to comply with age-related anatomical and functional differences in pediatric DBS populations. We illustrated their utility in a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources. MethodsAn average pediatric brain template (the MNI brain template 4.5–18.5 years) was implemented and used to localize the DBS electrodes in 20 patients from the GEPESTIM registry cohort. A pediatric subcortical atlas, analogous to the DISTAL atlas known in DBS research, was also employed to highlight the anatomical structures of interest. A local pallidal sweetspot was modeled, and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcomes. Additionally, a pediatric functional connectome of 100 neurotypical subjects from the Consortium for Reliability and Reproducibility was built to allow network-based analyses and decipher a connectivity fingerprint responsible for the clinical improvements in our cohort. ResultsWe successfully implemented a pediatric neuroimaging dataset that will be made available for public use as a tool for DBS analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). The functional connectivity fingerprint of DBS outcomes was determined to be a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003). ConclusionsLocal sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcomes in dystonia using pediatric neuroimaging surrogate data. Implementation of this pediatric neuroimaging dataset might help to improve the practice and pave the road towards a personalized DBS-neuroimaging analyses in pediatric patients.

Single-sequence protein structure prediction using supervised transformer protein language models.

Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.

Early Validation of a Structural Magnetic Resonance Imaging Metric for Tracking Dementia‐Related Neurodegeneration

AbstractBackgroundCurrent research is focused on noninvasive biomarkers of neurodegeneration that can detect dementia early, benchmark disease severity, monitor disease progression, and aid in testing the efficacy of therapeutic interventions utilizing a single number1–3. The objective of this study is to create and begin to validate an intuitively meaningful biomarker called the ADNeuro‐Score.MethodCognitively normal (CN) individuals and patients with a mild cognitive impairment (MCI) or dementia diagnosis were drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI)4. Eighty‐four cortical and subcortical regional volumes were estimated from T1‐weighted MR images using FreeSurfer5. To determine which regional volumes were associated with cognitive decline, we randomly selected 150 participants (N=50 each CN, MCI, and dementia) and performed an ANOVA with an alpha=0.05, Bonferroni corrected. A vector containing the resulting 41 regions, consistent with the existing literature6,7,8, was extracted for experimental and template cohorts (Table 1). To compute ADNeuro‐Score, a Hausdorff distance metric was used to estimate the differences between individual and average template vectors. ADNeuro‐Score was benchmarked using adjusted hippocampal volume (AHV), which is a NIA‐AA diagnostic biomarker for Alzheimer’s disease9, the most common form of dementia10. Both ADNeuro‐Score and AHV were harmonized for intracranial volume, age, sex, and scanner model11. Validation used an experimental cohort (N=929, mean age=72.67 years) and tested sensitivity to diagnosis using pairwise t‐tests and an alpha=0.001, Bonferroni corrected. Results were converted to a z‐score. We also tested association with disease severity, operationalized here as MMSE and ADASCog scores, using linear regression.ResultBoth ADNeuro‐Score and AHV differed between all three cognitive groups. ADNeuro‐Score (z=10.0) better differentiated MCI from dementia than AHV (z=8.9). AHV (z=7.3) was slightly better at distinguishing MCI from CN than ADNeuro‐Score (z=6.7). ADNeuro‐Score and AHV were similarly associated with MMSE (RADNS=0.41; RHA=0.41) and ADAS‐Cog (RADNS=0.49; RHA=0.47) scores.ConclusionThe newly developed ADNeuro‐Score seems to be a robust and reliable way to distinguish CN, MCI and AD patients and performs equivalently to AHV in predicting MMSE and ADAS‐Cog assessment scores. We hope ADNeuro‐Score will be highly specific to AD because of its focus on AD‐effected regions. Future research will determine if ADNeuro‐Score can improve the differential diagnosis of AD.