The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood.

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes.

Introduction: In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54). Methods: We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups. Results: As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups. Discussion: Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain.

Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.

Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression.

We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis.

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.

Cross-Validation of Functional MRI and Paranoid-Depressive Scale: Results From Multivariate Analysis.

Introduction: There exists over the past decades a constant debate driven by controversies in the validity of psychiatric diagnosis. This debate is grounded in queries about both the validity and evidence strength of clinical measures. Materials and Methods: The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). Results: We have been able to separate the two investigated clinical entities - schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. Following the individual and group MLM, we identified the three brain patterns that summarized all the individual variabilities of the individual brain patterns. Discussion: This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.

Patellar bone strain after total knee arthroplasty is correlated with bone mineral density and body mass index.

Patella-related complications after total knee arthroplasty (TKA) remain a major clinical concern. Previous studies have suggested that increased postoperative patellar bone strain could be related to such complications, but there is limited knowledge on patellar strain after TKA. The objective of this study was to predict patellar bone strain after TKA and evaluate correlations with various preoperative data. Fourteen TKA patients with a minimum follow-up of one year were included in this study. Using preoperative CT datasets, preoperative planning, and postoperative X-rays, a method is presented to generate patient-specific finite element models after virtual TKA. Patellar kinematics and forces were predicted during a squat movement, and patellar bone strain was evaluated at 60° of knee flexion. Strain varied greatly among patients, but was strongly negatively correlated (r = -0.85, p < 0.001) with bone mineral density (BMD) and moderately positively (r  = 0.54, p  =  0.05) with body mass index (BMI). The BMI/BMD ratio explained 87% of strain, and should be further investigated as a potential risk factor for clinical complications. This study represents a preliminary step towards the identification of patients at risk of patellar complications after TKA.

Effect of changes of femoral offset on abductor and joint reaction forces in total hip arthroplasty.

BACKGROUND: Anatomical reconstruction in total hip arthroplasty (THA) allows for physiological muscle function, good functional outcome and implant longevity. Quantitative data on the effect of a loss or gain of femoral offset (FO) are scarce. The aim of this study was to quantitatively describe the effect of FO changes on abductor moment arms, muscle and joint reactions forces. METHODS: THA was virtually performed on 3D models built from preoperative CT scans of 15 patients undergoing THA. Virtual THA was performed with a perfectly anatomical reconstruction, a loss of 20% of FO (-FO), and a gain of 20% of FO (+FO). These models were combined with a generic musculoskeletal model (OpenSim) to predict moment arms, muscle and joint reaction forces during normal gait cycles. RESULTS: In average, with -FO reconstructions, muscle moment arms decreased, while muscle and hip forces increased significantly (p < 0.001). We observed the opposite with +FO reconstructions. Gluteus medius was more affected than gluteus minimus. -FO had more effect than +FO. A change of 20% of FO induced an average change 8% of abductor moment arms, 16% of their forces, and 6% of the joint reaction force. CONCLUSIONS: To our knowledge, this is the first report providing quantitative data on the effect of FO changes on muscle and joint forces during normal gait. A decrease of FO necessitates an increase of abductor muscle force to maintain normal gait, which in turn increases the joint reaction force. This effect underscores the importance of an accurate reconstruction of the femoral offset.

Dual mobility cups provide biomechanical advantages in situations at risk for dislocation: a finite element analysis.

PURPOSE: Constrained devices, standard implants with large heads, and dual mobility systems have become popular options to manage instability after total hip arthroplasty (THA). Clinical results with these options have shown variable success rates and significant higher rates of aseptic loosening and mechanical failures with constrained implants. Literature suggests potential advantages of dual mobility, however little is known about its biomechanics. We present a comparative biomechanical study of a standard implant, a constrained implant, and a dual mobility system. METHODS: A finite element analysis was developed to assess and compare these acetabular options with regard to the range of motion (ROM) to impingement, the angle of dislocation, the resistive torque, the volume of polyethylene (PE) with a stress above 80% of the elastic limit, and the interfacial cup/bone stress. RESULTS: Dual mobility implants provided the greatest ROM to impingement and allowed delaying subluxation and dislocation when compared to standard and constrained implants. Dual mobility also demonstrated the lowest resistive torque at subluxation while the constrained implant provided the greatest one. The lowest critical PE volume was observed with the dual mobility implant, and the highest stress at the interfaces was observed with the constrained implant. CONCLUSION: This study highlights the biomechanical advantages of dual mobility systems over constrained and standard implants, and is supported by the clinical results reported. Therefore, the use of dual mobility systems in situations at risk for instability should be advocated and constrained implants should be restricted to salvage situations.

Identification of elastic properties of human patellae using micro-finite element analysis.

Current homogenized finite element (hFE) models of the patella lack a validated material law and mostly overlook trabecular anisotropy. The objective of this study was to identify the elastic constants of patellar trabecular bone. Using µCT scans of 20 fresh-frozen cadaveric patellae, we virtually extracted 200 trabecular cubes (5.3mm side length). Bone volume fraction and fabric tensor were measured. The elastic constants were identified from six independent load cases using micro finite element (µFE) analyses. Both anisotropic and isotropic material laws were considered. The elastic constants were validated by comparing stiffness, strain and stress between hFE and µFE predictions of 18 patellar sections and six load cases. The hFE section models were built from µCT (anisotropic law) and CT (isotropic law) scans. The homogenized anisotropic model induced less error (13±5%) in the global stiffness prediction than the isotropic one (18±6%), and less error in the prediction of local apparent strain, stress, and strain energy, compared to the isotropic one. This validated hFE model could be used for future applications, either with the anisotropic constants, or with the isotropic ones when the trabecular fabric is unavailable.

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration.

Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.

A patient-specific model of total knee arthroplasty to estimate patellar strain: A case study.

BACKGROUND: Inappropriate patellar cut during total knee arthroplasty can lead to patellar complications due to increased bone strain. In this study, we evaluated patellar bone strain of a patient who had a deeper patellar cut than the recommended. METHODS: A patient-specific model based on patient preoperative data was created. The model was decoupled into two levels: knee and patella. The knee model predicted kinematics and forces on the patella during squat movement. The patella model used these values to predict bone strain after total knee arthroplasty. Mechanical properties of the patellar bone were identified with micro-finite element modeling testing of cadaveric samples. The model was validated with a robotic knee simulator and postoperative X-rays. For this patient, we compared the deeper patellar cut depth to the recommended one, and evaluated patellar bone volume with octahedral shear strain above 1%. FINDINGS: Model predictions were consistent with experimental measurements of the robotic knee simulator and postoperative X-rays. Compared to the recommended cut, the deeper cut increased the critical strain bone volume, but by less than 3% of total patellar volume. INTERPRETATION: We thus conclude that the predicted increase in patellar strain should be within an acceptable range, since this patient had no complaints 8 months after surgery. This validated patient-specific model will later be used to address other questions on groups of patients, to eventually improve surgical planning and outcome of total knee arthroplasty.

Time course of bone screw fixation following a local delivery of Zoledronate in a rat femoral model - a micro-finite element analysis.

A good fixation of osteosynthesis implants is crucial for a successful bone healing but often difficult to achieve in osteoporotic patients. One possible solution to this issue is the local delivery of bisphosphonates in direct proximity to the implants, A critical aspect of this method, that has not yet been well investigated, is the time course of the implant fixation following the drug release. Usual destructive mechanical tests require large numbers of animals to produce meaningful results. Therefore, a micro-finite element (microFE) approach was chosen to analyze implant fixation. In vivo micro computed tomography (microCT) scans were obtained, first weekly and later bi-weekly, after implantation of polymeric screws in the femoral condyles of ovariectomized rats. In one half of the animals, Zoledronate was released from a hydrogel matrix directly in the peri-implant bone stock, the other animals were implanted only with screws as control. The time course of the implant fixation was investigated with linear elastic microFE models that were created based on in vivo microCT scans. The numerical models were validated against experimental pullout-tests measurements in an additional cadaver study. The microFE analysis revealed a significant increase in force at yield of the Zoledronate treated group compared to the control group. The force of the treated group was 28% higher after 17 days of screw implantation, 42% higher after 31 days. The significant difference persisted until the end of the in vivo study at day 58 (p<0.01). The early onset and prolonged duration of the implant anchorage improvement that was found in this study indicates the great potential of Zoledronate-loaded hydrogel for an enhancement of osteosynthesis implant fixation in impaired bone.