A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

IRMA: Machine learning-based harmonization of 18 F-FDG PET brain scans in multi-center studies

PURPOSE

Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.

METHODS

We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain18 F-Fluorodeoxyglucose (18 F-FDG) PET scans. The center difference is learned by applying IRMA on PCA-based feature vectors of healthy controls (HC), resulting in a subspace V , representing information not comparable between centers, and the remaining subspace U , where no center differences are present. In this proof-of-concept study, we demonstrate the properties of the method using data from four centers. After center-harmonization, a Generalized Matrix Learning Vector Quantization (GMLVQ) model was trained to discriminate between Parkinson's disease, Alzheimer's disease and Dementia with Lewy Bodies.

RESULTS

At the initial IRMA iteration, the system was able to determine the center origin of the four HC cohorts almost perfectly. The method required six iterations, corresponding to a six-dimensional subspace V , to determine the entire center difference. An uncorrected disease classification model was highly biased to center-specific effects, creating a falsely inflated performance when applying internal (cross-) validation. The cross-validation performance of the center-harmonized model remained high, while it generalized significantly better to unseen test cohorts. Furthermore, the framework is highly transparent, providing analytic reconstructions of the correction and visualizations of the data in voxel space.

CONCLUSION

IRMA can be used to learn and disregard center-specific information in features extracted from brain18 F-FDG PET scans, while retaining disease-specific information.

Rethinking MRI as a measurement device through modular and portable pipelines

The premise of MRI as a reliable measurement device is limited by proprietary barriers and inconsistent implementations, which prevent the establishment of measurement uncertainties. As a result, biomedical studies that rely on these methods are plagued by systematic variance, undermining the perceived promise of quantitative imaging biomarkers (QIBs) and hindering their clinical translation. This review explores the added value of open-source measurement pipelines in minimizing variability sources that would otherwise remain unknown. First, we introduce a tiered benchmarking framework (from black-box to glass-box) that exposes how opacity at different workflow stages propagates measurement uncertainty. Second, we provide a concise glossary to promote consistent terminology for strategies that enhance reproducibility before acquisition or enable valid post-hoc pooling of QIBs. Building on this foundation, we present two illustrative measurement workflows that decouple workflow logic from the orchestration of computational processes in an MRI measurement pipeline, rooted in the core principles of modularity and portability. Designed as accessible entry points for implementation, these examples serve as practical guides, helping users adapt the frameworks to their specific needs and facilitating collaboration. Through critical evaluation of existing approaches, we discuss how standardized workflows can help identify outstanding challenges in translating glass-box frameworks into clinical scanner environments. Ultimately, achieving this goal will require coordinated efforts from QIB developers, regulators, industry partners, and clinicians alike.

LRRK2 mutation contributes to decreased free water in the nucleus basalis of Meynert in manifest and premanifest Parkinson's disease

BACKGROUND

Free-water imaging can predict and monitor dopamine system degeneration in patients with Parkinson's disease (PD). However, brain cholinergic function has not been investigated to date in LRRK2 mutation carriers with or without PD using free-water imaging.

OBJECTIVES

To investigate the effect of LRRK2 mutations on the cholinergic system in manifest and premanifest stages of PD using free-water imaging.

METHODS

We recruited participants from the Parkinson's Progression Markers Initiative (PPMI) data set. We evaluated the effect of LRRK2 mutations on the cholinergic nuclei (i.e., cholinergic nuclei 1, 2, and 3 (Ch123), Ch4, and pedunculopontine nucleus) in manifest and premanifest stages of PD using free-water imaging. We compared free-water values between groups using ANCOVA with adjustment for age. Then, the discriminative power of the free-water content was evaluated by receiver operating characteristic curve (ROC) analysis.

RESULTS

We included 27 patients with LRRK2 PD, 33 LRRK2 mutation carriers without PD, 281 patients with idiopathic PD, and 98 healthy controls. We noted significant between-group differences in free-water content in Ch4 (p = 0.003). LRRK2 mutation carriers without PD had decreased free-water content in the Ch4 compared with healthy controls (p = 0.036) and idiopathic patients with PD (p = 0.001); LRRK2 patients with PD showed decreased tendency of free-water content in the Ch4 compared with idiopathic patients with PD (p = 0.074). Furthermore, ROC analysis showed that free-water content in the Ch4 identified asymptomatic LRRK2 mutation carriers with a high specificity (84.7%).

CONCLUSIONS

LRRK2 mutation is associated with decreased free-water content in the Ch4 (also referred to as nucleus basalis of Meynert, nbM), which might suggest early and sustained attempts to compensate for LRRK2-related dysfunction.

Cross-species alignment along the chronological axis reveals evolutionary effect on structural development of the human brain

Disentangling the evolution mysteries of the human brain has always been an imperative endeavor in neuroscience. Although many previous comparative studies revealed genetic, brain structural and connectivity distinctness between human and other nonhuman primates, the brain evolutional mechanism is still largely unclear. Here, we proposed to embed the brain anatomy of human and macaque in the developmental chronological axis to construct cross-species predictive model to quantitatively characterize brain evolution using two large public human and macaque datasets. We observed that applying the trained models within-species could well predict the chronological age. Interestingly, we found the model trained in macaque showed a higher accuracy in predicting the chronological age of human than the model trained in human in predicting the chronological age of macaque. The cross-application of the trained model introduced an individual brain cross-species age gap index to quantify the cross-species discrepancy along the temporal axis of brain development and was found to be associated with the behavioral performance in visual acuity test and picture vocabulary test in human. Taken together, our study situated the cross-species brain development along the chronological axis, which highlighted the disproportionately anatomical development in human brain to extend our understanding of the potential evolutionary effects.

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis

Accurate and generalizable classification of brain states is essential for understanding their neural underpinnings and improving clinical diagnostics. Traditionally, functional connectivity patterns and graph-theoretic metrics have been utilized. However, cortical gradient features, which reflect global brain organization, offer a complementary approach. We hypothesized that a machine learning model integrating these three feature sets would effectively discriminate between baseline and atypical brain states across a wide spectrum of conditions, even though the underlying neural mechanisms vary. To test this, we extracted features from brain states associated with three meta-conditions including unconsciousness (NREM2 sleep, propofol deep sedation, and propofol general anesthesia), psychedelic states induced by hallucinogens (subanesthetic ketamine, lysergic acid diethylamide, and nitrous oxide), and neuropsychiatric disorders (attention-deficit hyperactivity disorder, bipolar disorder, and schizophrenia). We used support vector machine with nested cross-validation to construct our models. The soft voting ensemble model marked the average balanced accuracy (average of specificity and sensitivity) of 79% (62-98% across all conditions), outperforming individual base models (70-76%). Notably, our models exhibited varying degrees of transferability across different datasets, with performance being dependent on the specific brain states and feature sets used. Feature importance analysis across meta-conditions suggests that the underlying neural mechanisms vary significantly, necessitating tailored approaches for accurate classification of specific brain states. This finding underscores the value of our feature-integrated ensemble models, which leverage the strengths of multiple feature types to achieve robust performance across a broader range of brain states. While our approach offers valuable insights into the neural signatures of different brain states, future work is needed to develop and validate even more generalizable models that can accurately classify brain states across a wider array of conditions.

Association between metabolic syndrome and white matter integrity in young and mid-age post-9/11 adult Veterans

Metabolic syndrome has been associated with reduced brain white matter integrity in older individuals. However, less is known about how metabolic syndrome might impact white matter integrity in younger populations. This study examined metabolic syndrome-related global and regional white matter integrity differences in a sample of 537 post-9/11 Veterans. Metabolic syndrome was defined as ≥3 factors of: increased waist circumference, hypertriglyceridemia, low high-density lipoprotein cholesterol, hypertension, and high fasting glucose. T1 and diffusion weighted 3 T MRI scans were processed using the FreeSurfer image analysis suite and FSL Diffusion Toolbox. Atlas-based regions of interest were determined from a combination of the Johns Hopkins University atlas and a Tract-Based Spatial Statistics-based FreeSurfer WMPARC white matter skeleton atlas. Analyses revealed individuals with metabolic syndrome (n = 132) had significantly lower global fractional anisotropy than those without metabolic syndrome (n = 405), and lower high-density lipoprotein cholesterol levels was the only metabolic syndrome factor significantly related to lower global fractional anisotropy levels. Lobe-specific analyses revealed individuals with metabolic syndrome had decreased fractional anisotropy in frontal white matter regions compared with those without metabolic syndrome. These findings indicate metabolic syndrome is prevalent in this sample of younger Veterans and is related to reduced frontal white matter integrity. Early intervention for metabolic syndrome may help alleviate adverse metabolic syndrome-related brain and cognitive effects with age.

ComBating inter-site differences in field strength: harmonizing preclinical traumatic brain injury MRI data

Integrating datasets from multiple sites and scanners can increase statistical power for neuroimaging studies but can also introduce significant inter-site confounds. We evaluated the effectiveness of ComBat, an empirical Bayes approach, to combine longitudinal preclinical MRI data acquired at 4.7 or 9.4 T at two different sites in Australia. Male Sprague Dawley rats underwent MRI on Days 2, 9, 28, and 150 following moderate/severe traumatic brain injury (TBI) or sham injury as part of Project 1 of the NIH/NINDS-funded Centre Without Walls EpiBioS4Rx project. Diffusion-weighted and multiple-gradient-echo images were acquired, and outcomes included QSM, FA, and ADC. Acute injury measures including apnea and self-righting reflex were consistent between sites. Mixed-effect analysis of ipsilateral and contralateral corpus callosum (CC) summary values revealed a significant effect of site on FA and ADC values, which was removed following ComBat harmonization. Bland-Altman plots for each metric showed reduced variability across sites following ComBat harmonization, including for QSM, despite appearing to be largely unaffected by inter-site differences and no effect of site observed. Following harmonization, the combined inter-site data revealed significant differences in the imaging metrics consistent with previously reported outcomes. TBI resulted in significantly reduced FA and increased susceptibility in the ipsilateral CC, and significantly reduced FA in the contralateral CC compared with sham-injured rats. Additionally, TBI rats also exhibited a reversal in ipsilateral CC ADC values over time with significantly reduced ADC at Day 9, followed by increased ADC 150 days after injury. Our findings demonstrate the need for harmonizing multi-site preclinical MRI data and show that this can be successfully achieved using ComBat while preserving phenotypical changes due to TBI.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Controlling false discovery rate for mediator selection in high-dimensional data

The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

Cerebrovascular lesion loads and accelerated brain aging: insights into the cognitive spectrum

Introduction

White matter hyperintensities (WMHs) and cerebral microbleeds are widespread among aging population and linked with cognitive deficits in mild cognitive impairment (MCI), vascular MCI (V-MCI), and Alzheimer's disease without (AD) or with a vascular component (V-AD). In this study, we aimed to investigate the association between brain age, which reflects global brain health, and cerebrovascular lesion load in the context of pathological aging in diverse forms of clinically-defined neurodegenerative conditions.

Methods

We computed brain-predicted age difference (brain-PAD: predicted brain age minus chronological age) in the Comprehensive Assessment of Neurodegeneration and Dementia cohort of the Canadian Consortium on Neurodegeneration in Aging including 70 cognitively intact elderly (CIE), 173 MCI, 88 V-MCI, 50 AD, and 47 V-AD using T1-weighted magnetic resonance imaging (MRI) scans. We used a well-established automated methodology that leveraged fluid attenuated inversion recovery MRIs for precise quantification of WMH burden. Additionally, cerebral microbleeds were detected utilizing a validated segmentation tool based on the ResNet50 network, utilizing routine T1-weighted, T2-weighted, and T2* MRI scans.

Results

The mean brain-PAD in the CIE cohort was around zero, whereas the four categories showed a significantly higher mean brain-PAD compared to CIE, except MCI group. A notable association trend between brain-PAD and WMH loads was observed in aging and across the spectrum of cognitive impairment due to AD, but not between brain-PAD and microbleed loads.

Discussion

WMHs were associated with faster brain aging and should be considered as a risk factor which imperils brain health in aging and exacerbate brain abnormalities in the context of neurodegeneration of presumed AD origin. Our findings underscore the significance of novel research endeavors aimed at elucidating the etiology, prevention, and treatment of WMH in the area of brain aging.

Free-Water Imaging of the Nucleus Basalis of Meynert in Patients With Idiopathic REM Sleep Behavior Disorder and Parkinson Disease

BACKGROUND AND OBJECTIVES

Cognitive impairments are common in idiopathic REM sleep behavior disorder (iRBD), in which the cholinergic degeneration of nucleus basalis of Meynert (NBM) may play an important role. However, the progressive changes of NBM, the relationship between progressive NBM degeneration and progression of cognitive impairments, and whether degeneration of the NBM can predict cognitive decline in patients with iRBD remain unclear. This study aimed to investigate the cross-sectional and longitudinal microstructural alterations in the NBM of patients with iRBD using free-water imaging and whether free water in the NBM is related to cognitive, mood, and autonomic function.

METHODS

We compared the baseline free-water values in the NBM between 59 healthy controls (HCs), 57 patients with iRBD, 57 patients with Parkinson disease (PD) with normal cognition (PD-NC), and 64 patients with PD with cognitive impairment (PD-CI). Thirty patients with iRBD and 40 HCs had one longitudinal data. In patients with iRBD, we explored the associations between baseline and longitudinal changes of free-water values in the NBM and clinical characteristics and whether baseline free-water values in the NBM could predict cognitive decline.

RESULTS

IRBD, PD-NC, and PD-CI groups had significantly increased free-water values in the NBM compared with HCs, whereas PD-CI had higher free-water values compared with iRBD and PD-NC. In patients with iRBD, free-water values in the NBM were progressively elevated over follow-up and correlated with the progression of cognitive impairment and depression. Free-water values in the NBM could predict cognitive decline in the iRBD group. Furthermore, we found that patients with iRBD with cognitive impairment had higher relative change of free-water value in the NBM compared with patients with iRBD with normal cognition over follow-up.

DISCUSSION

This study proves that free-water values in the NBM are elevated cross-sectionally and longitudinally and are associated with the progression of cognitive impairment and depression in patients with iRBD. Moreover, the free-water value in the NBM can predict cognitive decline in patients with iRBD. Whether free-water imaging of the NBM has the potential to be a marker for monitoring progressive cognitive impairment and predicting the conversion to dementia in synucleinopathies needs further investigation.

Tryptophan challenge in individuals with schizophrenia and healthy controls: acute effects on circulating kynurenine and kynurenic acid, cognition and cerebral blood flow

Cognitive impairments predict poor functional outcomes in people with schizophrenia. These impairments may be causally related to increased levels of kynurenic acid (KYNA), a major metabolic product of tryptophan (TRYP). In the brain, KYNA acts as an antagonist of the of α7-nicotinic acetylcholine and NMDA receptors, both of which are involved in cognitive processes. To examine whether KYNA plays a role in the pathophysiology of schizophrenia, we compared the acute effects of a single oral dose of TRYP (6 g) in 32 healthy controls (HC) and 37 people with either schizophrenia (Sz), schizoaffective or schizophreniform disorder, in a placebo-controlled, randomized crossover study. We examined plasma levels of KYNA and its precursor kynurenine; selected cognitive measures from the MATRICS Consensus Cognitive Battery; and resting cerebral blood flow (CBF) using arterial spin labeling imaging. In both cohorts, the TRYP challenge produced significant, time-dependent elevations in plasma kynurenine and KYNA. The resting CBF signal (averaged across all gray matter) was affected differentially, such that TRYP was associated with higher CBF in HC, but not in participants with a Sz-related disorder. While TRYP did not significantly impair cognitive test performance, there was a trend for TRYP to worsen visuospatial memory task performance in HC. Our results demonstrate that oral TRYP challenge substantially increases plasma levels of kynurenine and KYNA in both groups, but exerts differential group effects on CBF. Future studies are required to investigate the mechanisms underlying these CBF findings, and to evaluate the impact of KYNA fluctuations on brain function and behavior. (Clinicaltrials.gov: NCT02067975).

Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.

Harmonization of multi-site functional MRI data with dual-projection based ICA model

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Probabilistic estimation for across-batch compatibility enhancement for amyloid PET

INTRODUCTION

It is necessary to accurately account for systematic differences due to variability in scanners, radiotracers, and acquisition protocols in multisite studies combining amyloid imaging data.

METHODS

We propose Probabilistic Estimation for Across-batch Compatibility Enhancement (PEACE), a fully Bayesian multimodal extension of the widely used ComBat harmonization model, and we apply it to harmonize regional amyloid positron emission tomography data from two scanners.

RESULTS

Simulations show that PEACE recovers true harmonized values better than ComBat, even for unimodal data. PEACE harmonization of multiscanner regional amyloid imaging data yields results that agree better with longitudinal data compared to ComBat, without removing the known biological effects of age or apolipoprotein E genotype.

DISCUSSION

PEACE outperforms ComBat in both unimodal and bimodal contexts, is applicable to multisite amyloid imaging data, and holds promise for the harmonization of other neuroimaging data over ComBat.

HIGHLIGHTS

We introduce PEACE, a fully Bayesian multimodal extension of ComBat harmonization.Simulations show that PEACE recovers true harmonized values better than ComBat.PEACE accurately harmonizes multiscanner regional amyloid imaging data.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation

PURPOSE

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods.

METHODS

One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test.

RESULTS

A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.

CONCLUSION

It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Free-Water Imaging of the Substantia Nigra in GBA Pathogenic Variant Carriers

BACKGROUND

Pathogenic variants in the glucocerebrosidase gene (GBA) have been identified as the most common genetic risk factor for Parkinson's disease (PD). However, the features of substantia nigra damage in GBA pathogenic variant carriers remain unclear.

OBJECTIVE

We aimed to evaluate the microstructural changes in the substantia nigra in non-manifesting GBA pathogenic variant carriers (GBA-NMC) and PD patients with GBA pathogenic variant (GBA-PD) with free-water imaging.

METHODS

First, we compared free water values in the posterior substantia nigra between non-manifesting non-carriers (NMNC, n = 29), GBA-NMC (n = 26), and GBA-PD (n = 16). Then, free water values in the posterior substantia nigra were compared between GBA-PD and early- (n = 19) and late-onset (n = 40) idiopathic PD (iPD) patients. Furthermore, we examined whether the baseline free water values could predict the progressions of clinical symptoms.

RESULTS

The free water values in the posterior substantia nigra were significantly higher in the GBA-NMC and GBA-PD groups compared to NMNC, and were significantly increased in the GBA-PD group than both early- and late-onset iPD. Free water values in the posterior substantia nigra could predict the progression of anxiety and cognitive decline in GBA-NMC and GBA-PD groups.

CONCLUSIONS

We demonstrate that free water values are elevated in the substantia nigra and predict the development of non-motor symptoms in GBA-NMC and GBA-PD. Our findings demonstrate that a significant nigral impairment already exists in GBA-NMC, and nigral injury may be more severe in GBA-PD than in iPD. These results support that free-water imaging can as a potential early marker of substantia nigra damage. © 2023 International Parkinson and Movement Disorder Society.

Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia

Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.

Increased Free Water in the Substantia Nigra in Asymptomatic LRRK2 G2019S Mutation Carriers

BACKGROUND

The alteration of substantia nigra (SN) degeneration in populations at risk of Parkinson's disease (PD) is unclear.

OBJECTIVE

We investigated free water (FW) values in the posterior SN (pSN) in asymptomatic LRRK2 G2019S mutation carriers.

METHODS

We analyzed diffusion imaging data from 28 asymptomatic LRRK2 G2019S mutation carriers and 30 healthy controls (HCs), whereas 11 asymptomatic LRRK2 G2019S carriers and 11 HCs were followed up. FW values in the pSN were measured and compared between the groups. The relationship between longitudinal changes in FW in the pSN and dopamine transporter striatal binding ratio (SBR) was analyzed.

RESULTS

FW values in the pSN were significantly elevated and kept increasing during follow-up in asymptomatic LRRK2 G2019S carriers. There was a negative correlation between FW changes in the left pSN and SBR changes in the left putamen.

CONCLUSION

FW in the pSN has the potential to be a progression imaging marker of early dopaminergic degeneration in the population at risk of PD. © 2022 International Parkinson and Movement Disorder Society.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

The impact of harmonization on radiomic features in Parkinson's disease and healthy controls: A multicenter study

Radiomics is a challenging development area in imaging field that is greatly capturing interest of radiologists and neuroscientists. However, radiomics features show a strong non-biological variability determined by different facilities and imaging protocols, limiting the reproducibility and generalizability of analysis frameworks. Our study aimed to investigate the usefulness of harmonization to reduce site-effects on radiomics features over specific brain regions. We selected T1-weighted magnetic resonance imaging (MRI) by using the MRI dataset Parkinson's Progression Markers Initiative (PPMI) from different sites with healthy controls (HC) and Parkinson's disease (PD) patients. First, the investigation of radiomics measure discrepancies were assessed on healthy brain regions-of-interest (ROIs) via a classification pipeline based on LASSO feature selection and support vector machine (SVM) model. Then, a ComBat-based harmonization approach was applied to correct site-effects. Finally, a validation step on PD subjects evaluated diagnostic accuracy before and after harmonization of radiomics data. Results on healthy subjects demonstrated a dependence from site-effects that could be corrected with ComBat harmonization. LASSO regressor after harmonization was unable to select any feature to distinguish controls by site. Moreover, harmonized radiomics features achieved an area under the receiving operating characteristic curve (AUC) of 0.77 (compared to AUC of 0.71 for raw radiomics measures) in distinguish Parkinson's patients from HC. We found a not-negligible site-effect studying radiomics of HC pre- and post-harmonization of features. Our validation study on PD patients demonstrated a significant influence of non-biological noise source in diagnostic performances. Finally, harmonization of multicenter radiomic data represent a necessary step to make analysis pipelines reliable and replicable for multisite neuroimaging studies.

Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders

Bipolar disorder (BD) and major depressive disorder (MDD) are severe psychiatric illnesses that share among their environmental risk factors the exposure to adverse childhood experiences (ACE). Exposure to ACE has been associated with long-term changes in brain structure and the immune response. In the lasts decades, brain abnormalities including alterations of white matter (WM) microstructure and higher levels of peripheral immune/inflammatory markers have been reported in BD and MDD and an association between inflammation and WM microstructure has been shown. However, differences in these measures have been reported by comparing the two diagnostic groups. The aim of the present study was to investigate the interplay between ACE, inflammation, and WM in BD and MDD. We hypothesize that inflammation will mediate the association between ACE and WM and that this will be different in the two groups. A sample of 200 patients (100 BD, 100 MDD) underwent 3T MRI scan and ACE assessment through Childhood Trauma Questionnaire. A subgroup of 130 patients (75 MDD and 55 BD) underwent blood sampling for the assessment of immune/inflammatory markers. We observed that ACE associated with higher peripheral levels of IL-2, IL-17, bFGF, IFN-γ, TNF-α, CCL3, CCL4, CCL5, and PDGF-BB only in the BD group. Further, higher levels of CCL3 and IL-2 associated with lower FA in BD. ACE were found to differently affect WM microstructure in the two diagnostic groups and to be negatively associated with FA and AD in BD patients. Mediation analyses showed a significant indirect effect of ACE on WM microstructure mediated by IL-2. Our findings suggest that inflammation may mediate the detrimental effect of early experiences on brain structure and different mechanisms underlying brain alterations in BD and MDD.

A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas.

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.

Combining MRI and cognitive evaluation to classify concussion in university athletes

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R² = 0.32, p < .001), and performed worse on tests of concentration (∆R² = 0.07, p < .05) and delayed memory (∆R² = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.