Structure-function Associations Research Articles

Mutant Huntingtin Drives Development of an Advantageous Brain Early in Life: Evidence in Support of Antagonistic Pleiotropy.

Huntington's disease (HD) is a neurodegenerative disease caused by a triplet repeat expansion within the gene huntingtin (HTT). Antagonistic pleiotropy is a theory of aging that posits that some genes, facilitating individual fitness early in life through adaptive evolutionary changes, also augment detrimental aging-related processes. Antagonistic pleiotropy theory may explain a positive evolutionary pressure toward functionally advantageous brain development that is vulnerable to rapid degeneration. The current study investigated antagonistic pleiotropy in HD using a years-to-onset paradigm in a unique sample of children and young adults at risk for HD. Cognitive, behavioral, motor, and brain structural measures from premanifest gene-expanded (n = 79) and gene nonexpanded (n = 112) participants (6-21 years) in the Kids-HD study were examined. All measures in the gene-expanded group were modeled using a mixed-effects regression approach to assess years-to-onset-based changes while controlling for normal growth. Simultaneously, structure-function associations were also examined. Decades from motor onset, gene-expanded participants showed significantly better cognitive, behavioral, and motor scores versus gene nonexpanded controls, along with larger cerebral volumes and cortical features. After this initial peak, a prolonged deterioration was observed in both functional and structural measures. Far from onset, brain measures were positively correlated with functional measures, supporting the view that functional advantages were mediated by structural differences. Mutant HTT may drive the development of a larger than normal brain that subserves superior early-life function. These findings support the antagonistic pleiotropy theory of HTT in HD, where this gene drives early advantage followed by accelerated aging processes. ANN NEUROL 2024;96:1006-1019.

Soy protein-polyphenols conjugates interaction mechanism, characterization, techno-functional and biological properties: An updated review

Soy protein is a promising nutritional source with improved functionality and bioactivities due to conjugation with polyphenols (PP)—the conjugates between soy protein and PP held by covalent and noncovalent bonds. Different approaches, including thermodynamics, spectroscopy, and molecular docking simulations, can demonstrate the outcomes and mechanism of these conjugates. The soy protein, PP structure, matrix properties (temperature, pH), and interaction mechanism alter the ζ-potential, secondary structure, thermal stability, and surface hydrophobicity of proteins and also improve the techno-functional properties such as gelling ability, solubility, emulsifying, and foaming properties. Soy protein-PP conjugates also reveal enhanced in vitro digestibility, anti-allergic, antioxidant, anticancer, anti-inflammatory, and antimicrobial activities. Thus, these conjugates may be employed as edible film additives, antioxidant emulsifiers, hydrogels, and nanoparticles in the food industry. Future research is needed to specify the structure-function associations of soy protein-PP conjugates that may affect their functionality and application in the food industry.

Characterizing Retinal Sensitivity and Structure in Congenital Stationary Night Blindness: A Combined Microperimetry and OCT Study.

To investigate the characteristics of microperimetry and optical coherence tomography (OCT) in congenital stationary night blindness (CSNB), as well as their structure-function association. This cross-sectional study included 32 eyes from 32 participants with CSNB, comprising 18 with complete CSNB and 14 with incomplete CSNB, along with 36 eyes from 36 CSNB-unaffected controls matched for age, sex, and spherical equivalent. Using MP-3 microperimetry, central retinal sensitivity was assessed within a 20° field, distributed across six concentric rings (0°, 2°, 4°, 6°, 8°, and 10°). OCT was used to analyze retinal and choroidal thickness. The study aimed to assess the overall and ring-wise retinal sensitivity, as well as choroidal and retinal thickness in CSNB and CSNB-unaffected controls, with a secondary focus on the relationship between retinal sensitivity and microstructural features on OCT. In comparison with CSNB-unaffected subjects, the overall and ring-wise retinal sensitivity as well as choroidal thickness were reduced in patients with CSNB (P < 0.001). Moreover, the central sensitivity in incomplete CSNB group was lower than in complete CSNB group (25.72 ± 3.93 dB vs. 21.92 ± 4.10dB; P < 0.001). The retinal thickness in the CSNB group was thinner outside the fovea compared with the CSNB-unaffected group. Multiple mixed regression analyses revealed that point-to-point retinal sensitivity was significantly correlated with BCVA (P = 0.002) and the corresponding retinal thickness (P = 0.004). Examination of retinal sensitivity and OCT revealed different spatial distribution profiles in CSNB and its subtypes. In CSNB eyes, retinal sensitivity on microperimetry was associated with retinal thickness on OCT.

Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.

To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma. This cross-sectional pilot study analyzed 170eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity. The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR. RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice. We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

Structure-function association patterns of the brain in individuals with different level of cognitive impairment

Structure-function association patterns of the brain in individuals with different level of cognitive impairment

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss.

Circumpapillary retinal nerve fiber layer thickness (RNFLT) measurement aids in the clinical diagnosis of glaucoma. Spectral domain optical coherence tomography (SD-OCT) machines measure RNFLT and provide normative color-coded plots. In this retrospective study, we investigate whether normative percentiles of RNFLT (pRNFLT) from Spectralis SD-OCT improve prediction of glaucomatous visual field loss over raw RNFLT. A longitudinal database containing OCT scans and visual fields from Massachusetts Eye & Ear glaucoma clinic patients was generated. Reliable OCT-visual field pairs were selected. Spectralis OCT normative distributions were extracted from machine printouts. Supervised machine learning models compared predictive performance between pRNFLT and raw RNFLT inputs. Regional structure-function associations were assessed with univariate regression to predict mean deviation (MD). Multivariable classification predicted MD, pattern standard deviation, MD change per year, and glaucoma hemifield test. There were 3016 OCT-visual field pairs that met the reliability criteria. Spectralis norms were found to be independent of age, sex, and ocular magnification. Regional analysis showed significant decrease in R2 from pRNFLT models compared to raw RNFLT models in inferotemporal sectors, across multiple regressors. In multivariable classification, there were no significant improvements in area under the curve of receiver operating characteristic curve (ROC-AUC) score with pRNFLT models compared to raw RNFLT models. Our results challenge the assumption that normative percentiles from OCT machines improve prediction of glaucomatous visual field loss. Raw RNFLT alone shows strong prediction, with no models presenting improvement by the manufacturer norms. This may result from insufficient patient stratification in tested norms. Understanding correlation of normative databases to visual function may improve clinical interpretation of OCT data.

Neighborhood Disadvantage and Neural Correlates of Threat and Reward Processing in Survivors of Recent Trauma

Differences in neighborhood socioeconomic characteristics are important considerations in understanding differences in risk vs resilience in mental health. Neighborhood disadvantage is associated with alterations in the function and structure of threat neurocircuitry. To investigate associations of neighborhood disadvantage with white and gray matter and neural reactivity to positive and negative stimuli in the context of trauma exposure. In this cross-sectional study, survivors of trauma who completed sociodemographic and posttraumatic symptom assessments and neuroimaging were recruited as part of the Advancing Understanding of Recovery After Trauma (AURORA) study between September 2017 and June 2021. Data analysis was performed from October 25, 2022, to February 15, 2023. Neighborhood disadvantage was measured with the Area Deprivation Index (ADI) for each participant home address. Participants completed separate threat and reward tasks during functional magnetic resonance imaging. Diffusion-weighted and high-resolution structural images were also collected. Linear models assessed the association of ADI with reactivity, microstructure, and macrostructure of a priori regions of interest after adjusting for income, lifetime trauma, sex at birth, and age. A moderated-mediation model tested whether ADI was associated with neural activity via microstructural changes and if this was modulated by PTSD symptoms. A total of 280 participants (183 females [65.4%]; mean [SD] age, 35.39 [13.29] years) completed the threat task and 244 participants (156 females [63.9%]; mean [SD] age, 35.10 [13.26] years) completed the reward task. Higher ADI (per 1-unit increase) was associated with greater insula (t274 = 3.20; β = 0.20; corrected P = .008) and anterior cingulate cortex (ACC; t274 = 2.56; β = 0.16; corrected P = .04) threat-related activity after considering covariates, but ADI was not associated with reward reactivity. Greater disadvantage was also associated with altered microstructure of the cingulum bundle (t274 = 3.48; β = 0.21; corrected P = .001) and gray matter morphology of the ACC (cortical thickness: t273 = -2.29; β = -0.13; corrected P = .02; surface area: t273 = 2.53; β = 0.13; corrected P = .02). The moderated-mediation model revealed that ADI was associated with ACC threat reactivity via cingulum microstructural changes (index of moderated mediation = -0.02). However, this mediation was only present in individuals with greater PTSD symptom severity (at the mean: β = -0.17; standard error = 0.06, t= -2.28; P = .007; at 1 SD above the mean: β = -0.28; standard error = 0.08; t = -3.35; P < .001). In this study, neighborhood disadvantage was associated with neurobiology that supports threat processing, revealing associations of neighborhood disadvantage with neural susceptibility for PTSD and suggesting how altered structure-function associations may complicate symptoms. Future work should investigate specific components of neighborhood disadvantage that may be associated with these outcomes.

Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV

The main protease (Mpro) of SARS-CoV-2 is essential for viral replication, which suggests that the Mpro is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 Mpro in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 Mpro in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 Mpro in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 Mpro enzymatic activity, with a half maximal inhibitory concentration (IC50) of 2.69 μM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. Mpro-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 Mpro enzymatic activity, with an IC50 of 3.05 μM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC50 of <3.5 μM. Thus, C1 shows potential as an effective Mpro inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 Mpro and MERS-CoV Mpro.

Structure-function associations between contrast sensitivity and widefield swept-source optical coherence tomography angiography in diabetic macular edema.

To evaluate the relationship between contrast sensitivity (CS) and widefield swept-source optical coherence tomography angiography (WF SS-OCTA) vascular metrics in diabetic macular edema (DME) was the purpose. This prospectively enrolled cross-sectional observational study included 61 eyes of 48 patients that were tested with the quantitative CS function (qCSF) test on the same day as imaging with WF SS-OCTA (PLEX® Elite 9000, Carl Zeiss Meditec) 3 × 3, 6 × 6, and 12 × 12mm scans. Outcomes included visual acuity (VA) and multiple qCSF metrics. Vascular metrics included vessel density (VD) and vessel skeletonized density (VSD) in the superficial (SCP) and deep capillary plexus (DCP) and whole retina (WR) and foveal avascular zone (FAZ) parameters. Mixed effects multivariable linear regression models controlling for age, lens status, and diabetic retinopathy stage were performed. Standardized beta coefficients were calculated by refitting the standardized data. SS-OCTA metrics had a significant association with CS and VA. The effect size of OCTA metrics was larger on CS compared to VA. For example, the standardized beta coefficients for VSD and CS at 3cpd (βSCP = 0.76, βDCP = 0.71, βWR = 0.72, p < 0.001) were larger than those for VA (βSCP = - 0.55, p < 0.001; βDCP = - 0.43, p = 0.004; βWR = - 0.50, p < 0.001). On 6 × 6mm images, AULCSF, CS at 3cpd, and CS at 6cpd were significantly associated with VD and VSD in all three slab types (SCP, DCP, and WR), while VA was not. Structure-function associations in patients with DME leveraging the qCSF device suggest microvascular changes on WF SS-OCTA are associated with larger changes in contrast sensitivity than VA.

Progressive generalized tonic-clonic seizures in a transgenic mouse model of adult-onset epilepsy: Implications for morphological changes in cortico-limbic and brainstem structures

Video/cortical electroencephalography (EEG) is monitored to assess progressive severity of generalized tonic clonic seizures (GTCSs) in a transgenic mouse model of adult-onset epilepsy with increased death risk. The mice overexpress the brain derived neurotrophic factor (BDNF) in the forebrain under the calcium/calmodulin dependent protein kinase 2a (termed TgBDNF) and develop GTCSs in response to tail suspension/cage agitation stimulation at 3–4 months of age. With successive GTCSs (a total of 16 across 10 weeks of assessment), seizures became more severe as evidenced by increased duration of postictal generalized EEG suppression (PGES) associated with loss of posture/consciousness. Mice also developed spike wave discharges with behavioral arrest during the seizure recovery that increased in duration as a function of number of GTCSs. Overall seizure duration (from preictal spike to offset of PGES) and ictal spectral power (full spectra) were also increased. Half of the TgBDNF mice expired following a long period of PGES at the last recorded GTCS. Seizure-evoked general arousal impairment was associated with a striking decrease in total number of gigantocellular neurons of the brainstem nucleus pontis oralis along with increase in volumes of the anterior cingulate cortex and dorsal dentate gyrus in severely convulsive TgBDNF mice compared to litter-matched WT controls and non-convulsive TgBDNF mice. The latter effect was accompanied with an increase in total number of hippocampal granule neurons. These results provide structure-function associations in an animal model of adult-onset GTCSs that progressively increase in severity with clinical relevance for sudden unexpected death following generalized seizures.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques. To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia. We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia. In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases. The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Structure-function relationship in glaucoma: Optical coherence tomography en face imaging vs. red-free fundus photography.

To analyse retinal nerve fibre layer (RNFL) defect measurements obtained from red-free fundus photography and optical coherence tomography (OCT) en face imaging, respectively, and to compare them for the strength of the structure-function association. Two hundred and fifty-six glaucomatous eyes of 256 patients with localized RNFL defect on red-free fundus photography were enrolled. A subgroup analysis included 81 highly myopic eyes (≤ -6.0 dioptres). Angular width of RNFL defect was compared between red-free fundus photography (i.e., red-free RNFL defect) and OCT en face imaging (i.e., en face RNFL defect). The correlation between angular width of each RNFL defect and functional outcomes, reported as mean deviation (MD) and pattern standard deviation (PSD), were assessed and compared. The angular width of en face RNFL defect was measured smaller than that of red-free RNFL defect in 91.0% eyes (mean difference, 19.98°). The association of en face RNFL defect with MD and PSD was stronger (R2 = 0.311 and R2 = 0.372, respectively) than that of red-free RNFL defect with MD and PSD (R2 = 0.162 and R2 = 0.137, respectively) (P < 0.05 for all). Especially in highly myopic eyes, the association of en face RNFL defect with MD and PSD was much stronger (R2 = 0.503 and R2 = 0.555, respectively) than that of red-free RNFL defect with MD and PSD (R2 = 0.216 and R2 = 0.166, respectively) (P < 0.05 for all). En face RNFL defect showed a higher correlation with severity of visual field loss than did red-free RNFL defect. The same dynamic was observed for highly myopic eyes.

Meta-analysis of the functional neuroimaging literature with probabilistic logic programming

Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis by designing NeuroLang: a domain-specific language to express and test hypotheses using probabilistic first-order logic programming. By leveraging formalisms found at the crossroads of artificial intelligence and knowledge representation, NeuroLang provides the expressivity to address a larger repertoire of hypotheses in a meta-analysis, while seamlessly modeling the uncertainty inherent to neuroimaging data. We demonstrate the language’s capabilities in conducting comprehensive neuroimaging meta-analysis through use-case examples that address questions of structure-function associations. Specifically, we infer the specific functional roles of three canonical brain networks, support the role of the visual word-form area in visuospatial attention, and investigate the heterogeneous organization of the frontoparietal control network.

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS’ R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis.

The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research. Moreover, recent efforts aiming to map the LPFC through meta-analysis are limited, either in scope or in the inferred specificity of structure-function associations. These limitations are in part due to the limited expressiveness of commonly-used data analysis tools, which restricts the breadth and complexity of questions that can be expressed in a meta-analysis. Here, we adopt NeuroLang, a novel approach to more expressive meta-analysis based on probabilistic first-order logic programming, to infer the organizing principles of the LPFC from 14,371 neuroimaging studies. Our findings reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second most variance in meta-analytic connectivity across the LPFC. Moreover, we identify a unimodal-to-transmodal spectrum of coactivation patterns along with a concrete-to-abstract axis of structure-function associations extending from caudal to rostral regions of the LPFC. Finally, we infer inter-hemispheric asymmetries along the principal rostrocaudal gradient, identifying hemisphere-specific associations with topics of language, memory, response inhibition, and sensory processing. Overall, this study provides a comprehensive meta-analytic mapping of the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.

Structure-function association between contrast sensitivity and retinal thickness (total, regional, and individual retinal layer) in patients with idiopathic epiretinal membrane.

To investigate structure-function associations between retinal thickness, visual acuity (VA), and contrast sensitivity (CS), using the quantitative contrast sensitivity function (qCSF) method in patients with idiopathic epiretinal membrane (ERM). Retrospective, cross-sectional observational study. Patients with a diagnosis of idiopathic ERM were included. Patients underwent complete ophthalmic examination, spectral-domain optical coherence tomography imaging (SD-OCT) (SPECTRALIS® Heidelberg), and CS testing using the qCSF method. Outcomes included area under the log CSF (AULCSF), contrast acuity (CA), and CS thresholds at 1, 1.5, 3, 6, 12, and 18 cycles per degree (cpd). A total of 102 eyes of 79 patients were included. Comparing standardized regression coefficients, retinal thickness in most ETDRS sectors was associated with larger reductions in AULCSF, CA, and CS thresholds at 3 and 6cpd than those in logMAR VA. These differences in effect on VA and CS metrics were more pronounced in the central subfield and inner ETDRS sectors. Among the retinal layers, increased INL thickness had the most detrimental effect on visual function, being significantly associated with reductions in logMAR VA, AULCSF, CA, and CS thresholds at 3 and 6cpd (all p < .01), as well as at 1.5 and 12cpd (p < .05). Retinal thickness seems to be associated with larger reductions in contrast sensitivity than VA in patients with ERM. Measured with the qCSF method, contrast sensitivity may serve as a valuable adjunct visual function metric for patients with ERM.

Structure-Function Dissociations of Human Hippocampal Subfield Stiffness and Memory Performance.

Aging and neurodegenerative diseases lead to decline in thinking and memory ability. The subfields of the hippocampus (HCsf) play important roles in memory formation and recall. Imaging techniques sensitive to the underlying HCsf tissue microstructure can reveal unique structure-function associations and their vulnerability in aging and disease. The goal of this study was to use magnetic resonance elastography (MRE), a noninvasive MR imaging-based technique that can quantitatively image the viscoelastic mechanical properties of tissue to determine the associations of HCsf stiffness with different cognitive domains across the lifespan. Eighty-eight adult participants completed the study (age 23-81 years, male/female 36/51), in which we aimed to determine which HCsf regions most strongly correlated with different memory performance outcomes and if viscoelasticity of specific HCsf regions mediated the relationship between age and performance. Our results revealed that both interference cost on a verbal memory task and relational memory task performance were significantly related to cornu ammonis 1-2 (CA1-CA2) stiffness (p = 0.018 and p = 0.011, respectively), with CA1-CA2 stiffness significantly mediating the relationship between age and interference cost performance (p = 0.031). There were also significant associations between delayed free verbal recall performance and stiffness of both the dentate gyrus-cornu ammonis 3 (DG-CA3; p = 0.016) and subiculum (SUB; p = 0.032) regions. This further exemplifies the functional specialization of HCsf in declarative memory and the potential use of MRE measures as clinical biomarkers in assessing brain health in aging and disease.SIGNIFICANCE STATEMENT Hippocampal subfields are cytoarchitecturally unique structures involved in distinct aspects of memory processing. Magnetic resonance elastography is a technique that can noninvasively image tissue viscoelastic mechanical properties, potentially serving as sensitive biomarkers of aging and neurodegeneration related to functional outcomes. High-resolution in vivo imaging has invigorated interest in determining subfield functional specialization and their differential vulnerability in aging and disease. Applying MRE to probe subfield-specific cognitive correlates will indicate that measures of subfield stiffness can determine the integrity of structures supporting specific domains of memory performance. These findings will further validate our high-resolution MRE method and support the potential use of subfield stiffness measures as clinical biomarkers in classifying aging and disease states.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

BackgroundHypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.ObjectiveWe aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.Materials and MethodsWe enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, , K+, Na+, anion gap, C-reactive protein) served as ground truth.ResultsRadiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.ConclusionThe constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Physiological symmetry of transcranial magnetic stimulation-evoked EEG spectral features.

Transcranial magnetic stimulation (TMS)-evoked EEG potentials (TEPs) have been used to study the excitability of different cortical areas (CAs) in humans. Characterising the interhemispheric symmetry of TMS-EEG may provide further understanding of structure-function association in physiological and pathological conditions. We hypothesise that, in keeping with the underlying cytoarchitectonics, TEPs in contralateral homologous CAs share similar, symmetric spectral features, whilst ipsilateral TEPs from different CAs diverge in their waveshape and frequency content. We performed single-pulse (<1Hz) navigated monophasic TMS, combined with high-density EEG with active electrodes, in 10 healthy participants. We targeted two bilateral CAs: premotor and motor. We compared frequency power bands, computed Pearson correlation coefficient (R) and Correlated Component Analysis (CorrCA) to detect divergences, as well as common components across TEPs. The main frequency of TEPs was faster in premotor than in motor CAs (p < .05) across all participants. Frequencies were not different between contralateral homologous CAs, whilst, despite closer proximity, there was a significant difference between ipsilateral premotor and motor CAs (p > .5), with frequency decreasing from anterior to posterior CAs. Correlation was high between contralateral homologous CAs and low between ipsilateral CAs. When applying CorrCA, specific components were shared by contralateral homologous TEPs. We show physiological symmetry of TEP spectral features between contralateral homologous CAs, whilst ipsilateral premotor and motor TEPs differ despite lower geometrical distance. Our findings support the role of TEPs as biomarker of local cortical properties and provide a first reference dataset for TMS-EEG studies in asymmetric brain disorders.

Retinal Nerve Fiber Layer Optical Texture Analysis: Involvement of the Papillomacular Bundle and Papillofoveal Bundle in Early Glaucoma

To apply retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) in eyes with early glaucoma to investigate (1) the pattern of RNFL defects, (2) how often the papillomacular bundle and papillofoveal bundles are involved, and (3) the association between papillomacular and papillofoveal bundle defects and visual field (VF) sensitivity abnormalities. Cross-sectional study. Two hundred four eyes with early glaucoma (VF mean deviation, ≥ -6 dB) with RNFL defects from 171 consecutively enrolled patients with glaucoma. All eyes underwent 24-2 VF testing and OCT for ROTA. The borders of RNFL defects were delineated from ROTA, and the involvement of the arcuate bundle, papillomacular bundle (i.e., bundles from the macula, excluding the fovea), and papillofoveal bundle (i.e., bundles from the fovea) was determined for each eye. Multilevel logistic regression analysis was applied to evaluate the structure-function association. Proportions of eyes with papillomacular or papillofoveal bundle defects. Of the 204 eyes, 71.6% and 17.2% demonstrated RNFL defects involving the papillomacular and papillofoveal bundles, respectively; 25.0% showed arcuate bundle defects without involvement of papillomacular or papillofoveal bundles. The pattern of RNFL defects was diverse; the most common was concomitant involvement of the inferior arcuate bundle and the inferior papillomacular bundle (20.6%). Papillomacular or papillofoveal bundle defects were not associated with VF defects (i.e., with ≥ 3 contiguous locations with abnormal VF sensitivity in the pattern deviation probability plot) in the corresponding hemifield, although VF sensitivity of any 1 of the central 4 VF locations of the 24-2 test, which were within the macula, was more likely to be abnormal (P < 0.05 in the pattern deviation probability plot; odds ratio, 12.5; 95% confidence interval, 7.0-22.5) when the VF stimulus was projected onto a papillomacular or papillofoveal bundle defect than that projected onto an intact papillomacular or papillofoveal bundle. Contrary to the conventional notion that the fovea and macula are not affected until the late stages of glaucoma, papillofoveal and papillomacular bundle defects were common in early glaucoma, and they were associated with central VF sensitivity loss at the corresponding VF test locations.