A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents.

Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.

Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations.

Purpose: Random matrix theory (RMT) is an increasingly useful tool for understanding large, complex systems. Prior studies have examined functional magnetic resonance imaging (fMRI) scans using tools from RMT, with some success. However, RMT computations are highly sensitive to a number of analytic choices, and the robustness of findings involving RMT remains in question. We systematically investigate the usefulness of RMT on a wide variety of fMRI datasets using a rigorous predictive framework. Approach: We develop open-source software to efficiently compute RMT features from fMRI images and examine the cross-validated predictive potential of eigenvalue and RMT-based features ("eigenfeatures") with classic machine-learning classifiers. We systematically vary pre-processing extent, normalization procedures, RMT unfolding procedures, and feature selection and compare the impact of these analytic choices on the distributions of cross-validated prediction performance for each combination of dataset binary classification task, classifier, and feature. To deal with class imbalance, we use the area under the receiver operating characteristic curve (AUROC) as the main performance metric. Results: Across all classification tasks and analytic choices, we find RMT- and eigenvalue-based "eigenfeatures" to have predictive utility more often than not (82.4% of median AUROCs>0.5; median AUROC range across classification tasks 0.47 to 0.64). Simple baseline reductions on source timeseries, by contrast, were less useful (58.8% of median AUROCs>0.5, median AUROC range across classification tasks 0.42 to 0.62). Additionally, eigenfeature AUROC distributions were overall more right-tailed than baseline features, suggesting greater predictive potential. However, performance distributions were wide and often significantly affected by analytic choices. Conclusions: Eigenfeatures clearly have potential for understanding fMRI functional connectivity in a wide variety of scenarios. The utility of these features is strongly dependent on analytic decisions, suggesting caution when interpreting past and future studies applying RMT to fMRI. However, our study demonstrates that the inclusion of RMT statistics in fMRI investigations could improve prediction performances across a wide variety of phenomena.

Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for performing hold-out validation-that also assesses the consistency of the sample-wise mistakes made by the learning algorithm-can assist in the evaluation and design of reliable and predictable AI models. The technique can be applied to the validation of any supervised learning classification application, and we demonstrate the use of the technique on a variety of example biomedical diagnostic applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies.

Morphological Abnormalities in Early-Onset Schizophrenia Revealed by Structural Magnetic Resonance Imaging.

Schizophrenia is a pathological condition characterized by delusions, hallucinations, and a lack of motivation. In this study, we performed a morphological analysis of regional biomarkers in early-onset schizophrenia, including cortical thicknesses, surface areas, surface curvature, and volumes extracted from T1-weighted structural magnetic resonance imaging (MRI) and compared these findings with a large cohort of neurotypical controls. Results demonstrate statistically significant abnormal presentation of the curvature of select brain regions in early-onset schizophrenia with large effect sizes, inclusive of the pars orbitalis, pars triangularis, posterior cingulate cortex, frontal pole, orbital gyrus, lateral orbitofrontal gyrus, inferior occipital gyrus, as well as in medial occipito-temporal, lingual, and insular sulci. We also observed reduced regional volumes, surface areas, and variability of cortical thicknesses in early-onset schizophrenia relative to neurotypical controls in the lingual, transverse temporal, cuneus, and parahippocampal cortices that did not reach our stringent standard for statistical significance and should be confirmed in future studies with higher statistical power. These results imply that abnormal neurodevelopment associated with early-onset schizophrenia can be characterized with structural MRI and may reflect abnormal and possibly accelerated pruning of the cortex in schizophrenia.

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. METHODS: Forty-eight post-operative children, median age 4 months (2.9-11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33-197), mean lactate level at admission of 22.3 mg/dL (range 6.3-71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. RESULTS: The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. CONCLUSIONS: The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning.

We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients' depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.

Cortical thickness abnormalities in attention deficit hyperactivity disorder revealed by structural magnetic resonance imaging: Newborns to young adults.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition for which we have an incomplete understanding, and so brain imaging methods, such as magnetic resonance imaging (MRI), may be able to assist in characterising and understanding the presentation of the brain in an ADHD population. Statistical and computational methods were used to compare participants with ADHD and neurotypical controls at a variety of developmental stages to assess detectable abnormal neurodevelopment potentially associated with ADHD and to assess our ability to diagnose and characterise the condition from real-world clinical MRI examinations. T1-weighted structural MRI examinations (n = 993; 0-31 years old [YO]) were obtained from neurotypical controls, and 637 examinations were obtained from patients with ADHD (0-26 YO). Measures of average (mean) regional cortical thickness were acquired, alongside the first reporting of regional cortical thickness variability (as assessed with the standard deviation [SD]) in ADHD. A comparison between the inattentive and combined (inattentive and hyperactive) subtypes of ADHD is also provided. A preliminary independent validation was also performed on the publicly available ADHD200 dataset. Relative to controls, subjects with ADHD had, on average, lowered SD of cortical thicknesses and increased mean thicknesses across several key regions potentially linked with known symptoms of ADHD, including the precuneus and supramarginal gyrus.

Magnetic resonance imaging demonstrates gyral abnormalities in Tourette syndrome.

Tourette syndrome (TS) is a neurological disorder characterized by involuntary and repetitive movements known as tics. A retrospective analysis of magnetic resonance imaging (MRI) scans from 39 children and adolescents with TS was performed and subsequently compared with MRI scans from 834 neurotypical controls. The purpose of this study was to identify any differences in the regions of motor circuitry in TS to further our understanding of their disturbances in motor control (i.e., motor tics). Measures of volume, cortical thickness, surface area, and surface curvature for specific motor regions were derived from each MRI scan. The results revealed increased surface curvature in the opercular part of the inferior frontal gyrus and the triangular part of the inferior frontal gyrus in the TS group compared with the neurotypical control group. These novel findings offer some of the first evidence for surface curvature differences in motor circuitry regions in TS, which may be associated with known motor and vocal tics.

Preoperative and postoperative high angular resolution diffusion imaging tractography of cerebellar pathways in posterior fossa tumors.

This study aimed to utilize high angular resolution diffusion magnetic resonance imaging (HARDI) tractography in the mapping of the pathways of the cerebellum associated with posterior fossa tumors (infratentorial neoplasms) and to determine whether it is useful for preoperative and postoperative evaluation. Retrospective data from 30 patients (age 2-16 yr) with posterior fossa tumor (17 low grade, 13 high grade) and 30 age-sex-matched healthy controls were used. Structural and diffusion-weighted images were collected at a 3-tesla scanner. Tractography was performed using Diffusion Toolkit software, Q-ball model, FACT algorithm, and angle threshold of 45 degrees. Manually assessed regions of interest were placed to identify reconstructed fiber pathways passing through the superior, medial, and inferior cerebellar peduncles for the preoperative, postoperative, and healthy control groups. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and track volume measures were obtained and analyzed. Statistically significant differences were found between the preop/postop, preop/control, and postop/control comparisons for the volume of the tracts in both groups. Displacement and disruption of the pathways seemed to differ in relation to the severity of the tumor. The loss of pathways after the operation was associated with selective resection during surgery due to tumor infiltration. There were no FA differences but significantly higher ADC in low-grade tumors, and no difference in both FA and ADC in high-grade tumors. The effects of posterior fossa tumors on cerebellar peduncles and reconstructed pathways were successfully evaluated by HARDI tractography. The technique appears to be useful not only for preoperative but also for postoperative evaluation.

Quantifying uncertainty in machine learning classifiers for medical imaging.

PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical implications. This paper applied, validated, and explored a technique for assessing uncertainty in convolutional neural networks (CNNs) in the context of MI. MATERIALS AND METHODS: We used two publicly accessible imaging datasets: a chest x-ray dataset (pneumonia vs. control) and a skin cancer imaging dataset (malignant vs. benign) to explore the proposed measure of uncertainty based on experiments with different class imbalance-sample sizes, and experiments with images close to the classification boundary. We also further verified our hypothesis by examining the relationship with other performance metrics and cross-checking CNN predictions and confidence scores with an expert radiologist (available in the Supplementary Information). Additionally, bounds were derived on the uncertainty metric, and recommendations for interpretability were made. RESULTS: With respect to training set class imbalance for the pneumonia MI dataset, the uncertainty metric was minimized when both classes were nearly equal in size (regardless of training set size) and was approximately 17% smaller than the maximum uncertainty resulting from greater imbalance. We found that less-obvious test images (those closer to the classification boundary) produced higher classification uncertainty, about 10-15 times greater than images further from the boundary. Relevant MI performance metrics like accuracy, sensitivity, and sensibility showed seemingly negative linear correlations, though none were statistically significant (p [Formula: see text] 0.05). The expert radiologist and CNN expressed agreement on a small sample of test images, though this finding is only preliminary. CONCLUSIONS: This paper demonstrated the importance of uncertainty reporting alongside predictions in medical imaging. Results demonstrate considerable potential from automatically assessing classifier reliability on each prediction with the proposed uncertainty metric.

A novel graph mining approach to predict and evaluate food-drug interactions.

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.

Structural abnormalities in paediatric moyamoya disease revealed by clinical magnetic resonance imaging, regionally distributed relative signal intensities and volumes.

Moyamoya disease (MMD) is a rare, progressive cerebrovascular disorder, with an unknown aetiology and pathogenesis. It is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals called moyamoya vessels. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize MMD clinically, with the help of regionally distributed relative signal intensities (RRSIs) and volumes (RRVs). These RRSIs and RRVs provide the ability to characterize aspects of regional brain development and represent an extension to existing automated biomarker extraction technologies. This study included 269 MRI examinations from MMD patients and 993 MRI examinations from neurotypical controls, with regional biomarkers compared between groups with the area under the receiver operating characteristic curve (AUC). Results demonstrate abnormal presentation of RRSIs and RRVs in the insula (15- to 20-year old cohort, left AUC: 0.74, right AUC: 0.71) and the lateral orbitofrontal region (5- to 10-year old cohort, left AUC: 0.67; 15-20 year cohort, left AUC: 0.62, right AUC: 0.65). Results indicate that RRSIs and RRVs may help in characterizing brain development, assist in the assessment of the presentation of the brains of children with MMD and help overcome standardization challenges in multiprotocol clinical MRI. Further investigation of the potential for RRSIs and RRVs in clinical imaging is warranted and supported through the release of open-source software.

Integration of structural MRI and epigenetic analyses hint at linked cellular defects of the subventricular zone and insular cortex in autism: Findings from a case study.

Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction, communication and repetitive, restrictive behaviors, features supported by cortical activity. Given the importance of the subventricular zone (SVZ) of the lateral ventrical to cortical development, we compared molecular, cellular, and structural differences in the SVZ and linked cortical regions in specimens of ASD cases and sex and age-matched unaffected brain. Methods: We used magnetic resonance imaging (MRI) and diffusion tractography on ex vivo postmortem brain samples, which we further analyzed by Whole Genome Bisulfite Sequencing (WGBS), Flow Cytometry, and RT qPCR. Results: Through MRI, we observed decreased tractography pathways from the dorsal SVZ, increased pathways from the posterior ventral SVZ to the insular cortex, and variable cortical thickness within the insular cortex in ASD diagnosed case relative to unaffected controls. Long-range tractography pathways from and to the insula were also reduced in the ASD case. FACS-based cell sorting revealed an increased population of proliferating cells in the SVZ of ASD case relative to the unaffected control. Targeted qPCR assays of SVZ tissue demonstrated significantly reduced expression levels of genes involved in differentiation and migration of neurons in ASD relative to the control counterpart. Finally, using genome-wide DNA methylation analyses, we identified 19 genes relevant to neurological development, function, and disease, 7 of which have not previously been described in ASD, that were significantly differentially methylated in autistic SVZ and insula specimens. Conclusion: These findings suggest a hypothesis that epigenetic changes during neurodevelopment alter the trajectory of proliferation, migration, and differentiation in the SVZ, impacting cortical structure and function and resulting in ASD phenotypes.

Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder.

Early interventions for autism spectrum disorder (ASD) are increasingly available, while only 42-50% of ASD children are diagnosed before 3 years old (YO). To identify neuroimaging biomarkers for early ASD diagnosis, we evaluated surface- and voxel-based brain morphometry in participants under 3YO who were later diagnosed with ASD. Magnetic resonance imaging data were retrospectively obtained from patients later diagnosed with ASD at Boston Children's Hospital. The ASD participants with comorbidities such as congenital disorder, epilepsy, and global developmental delay/intellectual disability were excluded from statistical analyses. Eighty-five structural brain magnetic resonance imaging images were collected from 81 participants under 3YO and compared with 45 images from 45 gender- and age-matched nonautistic controls (non-ASD). Using an Infant FreeSurfer pipeline, 236 regionally distributed measurements were extracted from each scan. By t-tests and linear mixed models, the smaller nucleus accumbens and larger bilateral lateral, third, and fourth ventricles were identified in the ASD group. Vertex-wise t-statistical maps showed decreased thickness in the caudal anterior cingulate cortex and increased thickness in the right medial orbitofrontal cortex in ASD. The smaller bilateral accumbens nuclei and larger cerebral ventricles were independent of age, gender, or gestational age at birth, suggesting that there are MRI-based biomarkers in prospective ASD patients before they receive the diagnosis and that the volume of the nucleus accumbens and cerebral ventricles can be key MRI-based early biomarkers to predict the emergence of ASD.

Structural magnetic resonance imaging demonstrates volumetric brain abnormalities in down syndrome: Newborns to young adults.

Down syndrome (DS) is a genetic disorder caused by the presence of an extra full or partial copy of chromosome 21 and characterized by intellectual disability. We hypothesize that performing a retrospective analysis of 73 magnetic resonance imaging (MRI) examinations of participants with DS (aged 0 to 22 years) and comparing them to a large cohort of 993 brain MRI examinations of neurotypical participants (aged 0 to 32 years), will assist in better understanding what brain differences may explain phenotypic developmental features in DS, as well as to provide valuable confirmation of prospective literature findings clinically. Measurements for both absolute volumes and volumes corrected as a percentage of estimated total intracranial volume (%ETIV) were extracted from each examination. Our results presented novel findings such as volume increases (%ETIV) in the perirhinal cortex, entorhinal cortex, choroid plexus, and Brodmann's areas (BA) 3a, 3b, and 44, as well as volume decreases (%ETIV) in the white matter of the cuneus, the paracentral lobule, the postcentral gyrus, and the supramarginal gyrus. We also confirmed volumetric brain abnormalities previously discussed in the literature. Findings suggest the presence of volumetric brain abnormalities in DS that can be detected clinically with MRI.

Cortical thickness in clinical moyamoya disease: A magnetic resonance imaging study.

Moyamoya disease (MMD) is a progressive cerebrovascular disorder, with an unknown pathogenesis and aetiology. MMD is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals, also known as moyamoya vessels. Patients with MMD show variable patterns of brain damage and may experience recurrent multiple transient ischaemic attacks, intracranial bleeding and cerebral infarction. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize abnormal cortical development in MMD clinically, with an analysis of both average and variability of regional cortical thicknesses. This study also included a machine learning analysis to assess the predictive capacity of the cortical thickness abnormalities observed in this research. This study included 993 MRI examinations from neurotypical controls and 269 MRI examinations from MMD patients. Results demonstrate abnormal cortical presentation of the insula, caudate, postcentral, precuneus and cingulate regions, in agreement with previous literature cortical thickness findings as well as alternative methods such as functional MRI (fMRI) and digital angiography. To the best of our knowledge, this is the first manuscript to report cortical thickness abnormalities in the middle temporal visual area in MMD and the first study to report on cortical thickness variability abnormalities in MMD.

Symptom-Related Differential Neuroimaging Biomarkers in Children with Corpus Callosum Abnormalities.

We aimed to identify symptom-related neuroimaging biomarkers for patients with dysgenesis of the corpus callosum (dCC) by summarizing neurological symptoms reported in clinical evaluations and correlating them with retrospectively collected structural/diffusion brain magnetic resonance imaging (MRI) measures from 39 patients/controls (mean age 8.08 ± 3.98). Most symptoms/disorders studied were associated with CC abnormalities. Total brain (TB) volume was related to language, cognition, muscle tone, and metabolic/endocrine abnormalities. Although white matter (WM) volume was not related to symptoms studied, gray matter (GM) volume was related to cognitive, behavioral, and metabolic/endocrine disorders. Right hemisphere (RH) cortical thickness (CT) was linked to language abnormalities, while left hemisphere (LH) CT was linked to epilepsy. While RH gyrification index (GI) was not related to any symptoms studied, LH GI was uniquely related to cognitive disorders. Between patients and controls, GM volume and LH/RH CT were significantly greater in dCC patients, while WM volume and LH/RH GI were significantly greater in controls. TB volume and diffusion indices for tissue microstructures did not show differences between the groups. In summary, our brain MRI-based measures successfully revealed differential links to many symptoms. Specifically, LH GI abnormality can be a predictor for dCC patients, which is uniquely associated with the patients' symptom. In addition, patients with CC abnormalities had normal TB volume and overall tissue microstructures, with potentially deteriorated mechanisms to expand/fold the brain, indicated by GI.

Hold-out validation for the assessment of stability and reliability of multivariable regression demonstrated with magnetic resonance imaging of patients with schizophrenia.

Neuroscience studies are very often tasked with identifying measurable differences between two groups of subjects, typically one group with a pathological condition and one group representing control subjects. It is often expected that the measurements acquired for comparing groups are also affected by a variety of additional patient characteristics such as sex, age, and comorbidities. Multivariable regression (MVR) is a statistical analysis technique commonly employed in neuroscience studies to "control for" or "adjust for" secondary effects (such as sex, age, and comorbidities) in order to ensure that the main study findings are focused on actual differences between the groups of interest associated with the condition under investigation. It is common practice in the neuroscience literature to utilize MVR to control for secondary effects; however, at present, it is not typically possible to assess whether the MVR adjustments correct for more error than they introduce. In common neuroscience practice, MVR models are not validated and no attempt to characterize deficiencies in the MVR model is made. In this article, we demonstrate how standard hold-out validation techniques (commonly used in machine learning analyses) that involve repeatedly randomly dividing datasets into training and testing samples can be adapted to the assessment of stability and reliability of MVR models with a publicly available neurological magnetic resonance imaging (MRI) dataset of patients with schizophrenia. Results demonstrate that MVR can introduce measurement error up to 30.06% and, on average across all considered measurements, introduce 9.84% error on this dataset. When hold-out validated MVR does not agree with the results of the standard use of MVR, the use of MVR in the given application is unstable. Thus, this paper helps evaluate the extent to which the simplistic use of MVR introduces study error in neuroscientific analyses with an analysis of patients with schizophrenia.

Clinically detectable structural abnormalities in pediatric-onset multiple sclerosis: A large-scale magnetic resonance imaging analysis.

BACKGROUND: Multiple Sclerosis is characterized by neural demyelination. Structural magnetic resonance imaging (MRI) provides soft tissue contrast, which forms the basis of techniques for extracting regional biomarkers across a participant's brain. OBJECTIVES: To investigate the clinical presentation of multiple sclerosis in a large-scale MRI analysis that includes thorough consideration of extractable structural measurements (average and variability of regional cortical thicknesses, cortical surface measurements, and volumes). METHODS: We performed a large-scale retrospective analysis of 370 T1 structural volumetric MRIs from 64 participants with multiple sclerosis and compared them with a large cohort of neurotypical participants, consisting of 993 MRIs from 988 participants. Regionally distributed measurements of cortical thickness (average and standard deviation) were extracted along with surface area, surface curvature, and volumetric measurements. RESULTS: The largest observed finding involved regionally distributed reductions in average cortical thickness, with the parahippocampal region exhibiting the largest effect size, a finding that may be linked with known hippocampal atrophy in multiple sclerosis. Group-wise differences were also observed in terms of distributed volume, surface area, and surface curvature measurements. CONCLUSIONS: Participants with pediatric-onset multiple sclerosis present clinically with a variety of structural abnormalities, including perirhinal cortex thickness abnormalities not previously reported in the literature.