Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain

Associations of haematological and inflammatory biomarkers with brain volume in patients with sickle cell anaemia: A cross-sectional retrospective study

Sickle cell disease is a genetic disorder characterised by abnormal haemoglobin production. This study aims to investigate the associations between haematological and inflammatory biomarkers and brain volumes in patients with sickle cell anaemia and compare brain structure between patients with sickle cell anaemia and healthy controls. This retrospective cross-sectional study included 130 participants (70 sickle cell anaemia patients and 60 healthy controls) who underwent brain MRI examinations at King Fahad Central Hospital between January 2010 and October 2022. Demographic data and haematological and inflammatory biomarkers were collected to examine their relationships with brain volumes. Brain volumes were measured using FreeSurfer. Specific haematological and inflammatory biomarkers were correlated with brain volume in patients with sickle cell anaemia, p < 0.05. Sickle cell anaemia patients exhibited smaller volumes in the brainstem, corpus callosum and amygdala compared to healthy controls. Males had significantly higher iron levels (p < 0.001) and larger various brain structure volumes (p < 0.05) than females. This study demonstrates significant associations between specific biomarkers and brain volume in sickle cell anaemia patients, underscoring the importance of monitoring these biomarkers for early detection and management of neurological complications in sickle cell anaemia.

Age-adjusted CSF t-tau and NfL do not improve diagnostic accuracy for prodromal Alzheimer's disease

Cerebrospinal fluid total-tau (t-tau) and neurofilament light chain (NfL) are biomarkers of neurodegeneration and are increased in Alzheimer's disease (AD). In order to adjust for age-related increases in t-tau and NfL, cross-sectional age-adjusted norms were developed based on amyloid negative cognitively normal (CN) adults aged 41-78 years (CN, n = 137). The age-adjusted norms for t-tau and NfL did not improve receiver operating curve based diagnostic accuracies in individuals with mild cognitive impairment (MCI) due to AD (AD-MCI, n = 144). Furthermore, while NfL was correlated with higher age in AD-MCI, no significant correlation was found for t-tau. The cox proportional hazard models, applied in 429 participants with baseline t-tau and NfL, showed higher hazard ratio of progression to MCI or dementia without age-adjustments (HR = 3.39 for t-tau and HR = 3.17 for NfL), as compared to using our norms (HR = 2.29 for t-tau and HR = 1.89 for NfL). Our results indicate that utilizing normative reference data could obscure significant age-related increases in these markers associated with neurodegeneration and AD leading to a potential loss of overall diagnostic accuracy.

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Sex, gender diversity, and brain structure in early adolescence

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry

PURPOSE

Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal ("NDB cleaning") and to test its impact on the performance of VBM for detection of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD).

METHODS

T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD.

RESULTS

Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36-0.58] to 0.61 [0.49-0.71]).

CONCLUSION

NDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Remembering the null hypothesis when searching for brain sex differences

Human brain sex differences have fascinated scholars for centuries and become a key focus of neuroscientists since the dawn of MRI. We recently published a major review in Neuroscience and Biobehavioral Reviews showing that most male-female brain differences in humans are small and few have been reliably replicated. Although widely cited, this work was the target of a critical Commentary by DeCasien et al. (Biol Sex Differ 13:43, 2022). In this response, I update our findings and confirm the small effect sizes and pronounced scatter across recent large neuroimaging studies of human sex/gender difference. Based on the sum of data, neuroscientists would be well-advised to take the null hypothesis seriously: that men and women's brains are fundamentally similar, or "monomorphic". This perspective has important implications for how we study the genesis of behavioral and neuropsychiatric gender disparities.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Cumulative Impact of Morphometric Features in Schizophrenia in Two Independent Samples

Schizophrenia and bipolar disorder share a common structural brain alteration profile. However, there is considerable between- and within-diagnosis variability in these features, which may underestimate informative individual differences. Using a recently established morphometric risk score (MRS) approach, we aim to provide confirmation that individual MRS scores are higher in individuals with a psychosis diagnosis, helping to parse individual heterogeneity. Using the Human Connectome Project Early Psychosis (N = 124), we estimate MRS for psychosis and specifically for bipolar/schizophrenia using T1-weighted MRI data and prior meta-analysis effect sizes. We confirm associations in an independent replication sample (N = 69). We assess (1) the impact of diagnosis on these MRS, (2) compare effect sizes of MRS to all individual, cytoarchitecturally defined brain regions, and (3) perform negative control analyses to assess MRS specificity. The MRS specifically for SCZ was higher in the whole psychosis group (Cohen's d = 0.56; P = 0.003) and outperformed any single region of interest in standardized mean difference (ZMRS>75 ROIS = 2.597; P = 0.009) and correlated with previously reported effect sizes (PSPIN/SHUFFLE < 0.005). MRS without Enhancing Neuroimaging Genomics through Meta-Analysis weights did not delineate groups with empirically null associations (t = 2.29; P = 0.02). We replicate MRS specifically for SCZ associations in the independent sample. Akin to polygenic risk scoring and individual allele effect sizes, these observations suggest that assessing the combined impact of regional structural alterations may be more informative than any single cytoarchitecturally constrained cortical region, where well-powered, meta-analytical samples are informative in the delineation of diagnosis and within psychosis case differences, in smaller independent samples.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors

Introduction

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG.

Methods

T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction.

Results

The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers.

Discussion

In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study

Background: Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice.

Methods: We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer’s disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard.

Results: Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75–0.58; p < 0.001) and increase of specificity (0.62–0.85; p < 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (p < 0.001).

Conclusions: Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.

Association of amygdala size with stress perception: Findings of a transversal study across the lifespan

Daily routines are getting increasingly stressful. Interestingly, associations between stress perception and amygdala volume, a brain region implicated in emotional behaviour, have been observed in both younger and older adults. Life stress, on the other hand, has become pervasive and is no longer restricted to a specific age group or life stage. As a result, it is vital to consider stress as a continuum across the lifespan. In this study, we investigated the relationship between perceived stress and amygdala size in 272 healthy participants with a broad age range. Participants were submitted to a structural magnetic resonance imaging (MRI) to extract amygdala volume, and the Perceived Stress Scale (PSS) scores were used as the independent variable in volumetric regressions. We found that perceived stress is positively associated with the right amygdala volume throughout life.

Uncovering sex/gender differences of arithmetic in the human brain: Insights from fMRI studies

Over the long run, STEM fields had been perceived as dominant by males, despite that numerous studies have shown that female students do not underperform their male classmates in mathematics and science. In this review, we discuss whether and how sex/gender shows specificity in arithmetic processing using a cognitive neuroscience approach not only to capture contemporary differences in brain and behavior but also to provide exclusive brain bases knowledge that is unseen in behavioral outcomes alone. We begin by summarizing studies that had examined sex differences/similarities in behavioral performance of mathematical learning, with a specific focus on large-scale meta-analytical data. We then discuss how the magnetic resonance imaging (MRI) approach can contribute to understanding neural mechanisms underlying sex-specific effects of mathematical learning by reviewing structural and functional data. Finally, we close this review by proposing potential research issues for further exploration of the sex effect using neuroimaging technology. Through the lens of advancement in the neuroimaging technique, we seek to provide insights into uncovering sex-specific neural mechanisms of learning to inform and achieve genuine gender equality in education.

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.

"Nothing to see here": No structural brain differences as a function of the Big Five personality traits from a systematic review and meta-analysis

Personality reflects social, affective, and cognitive predispositions that emerge from genetic and environmental influences. Contemporary personality theories conceptualize a Big Five Model of personality based on the traits of neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience. Starting around the turn of the millennium, neuroimaging studies began to investigate functional and structural brain features associated with these traits. Here, we present the first study to systematically evaluate the entire published literature of the association between the Big Five traits and three different measures of brain structure. Qualitative results were highly heterogeneous, and a quantitative meta-analysis did not produce any replicable results. The present study provides a comprehensive evaluation of the literature and its limitations, including sample heterogeneity, Big Five personality instruments, structural image data acquisition, processing, and analytic strategies, and the heterogeneous nature of personality and brain structures. We propose to rethink the biological basis of personality traits and identify ways in which the field of personality neuroscience can be strengthened in its methodological rigor and replicability.

Grey matter atrophy in patients with benign multiple sclerosis

BACKGROUND

Brain atrophy appears during the progression of multiple sclerosis (MS) and is associated with the disability caused by the disease.

METHODS

We investigated global and regional grey matter (GM) and white matter (WM) volumes, WM lesion load, and corpus callosum index (CCI), in benign relapsing-remitting MS (BRRMS, n = 35) with and without any treatment and compared those to aggressive relapsing-remitting MS (ARRMS, n = 46). Structures were analyzed by using an automated MRI quantification tool (cNeuro®).

RESULTS

The total brain and cerebral WM volumes were larger in BRRMS than in ARRMS (p = .014, p = .017 respectively). In BRRMS, total brain volumes, regional GM volumes, and CCI were found similar whether or not disease-modifying treatment (DMT) was used. The total (p = .033), as well as subcortical (p = .046) and deep WM (p = .041) lesion load volumes were larger in BRRMS patients without DMT. Cortical GM volumes did not differ between BRRMS and ARRMS, but the volumes of total brain tissue (p = .014) and thalami (p = .003) were larger in patients with BRRMS compared to ARRMS. A positive correlation was found between CCI and whole-brain volume in both BRRMS (r = .73, p < .001) and ARRMS (r = .80, p < .01).

CONCLUSIONS

Thalamic volume is the most prominent measure to differentiate BRRMS and ARRMS. Validation of automated quantification of CCI provides an additional applicable MRI biomarker to detect brain atrophy in MS.

Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms

Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.

Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions

Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.

Cognitive impairment and World Trade Centre-related exposures

On 11 September 2001 the World Trade Center (WTC) in New York was attacked by terrorists, causing the collapse of multiple buildings including the iconic 110-story 'Twin Towers'. Thousands of people died that day from the collapse of the buildings, fires, falling from the buildings, falling debris, or other related accidents. Survivors of the attacks, those who worked in search and rescue during and after the buildings collapsed, and those working in recovery and clean-up operations were exposed to severe psychological stressors. Concurrently, these 'WTC-affected' individuals breathed and ingested a mixture of organic and particulate neurotoxins and pro-inflammogens generated as a result of the attack and building collapse. Twenty years later, researchers have documented neurocognitive and motor dysfunctions that resemble the typical features of neurodegenerative disease in some WTC responders at midlife. Cortical atrophy, which usually manifests later in life, has also been observed in this population. Evidence indicates that neurocognitive symptoms and corresponding brain atrophy are associated with both physical exposures at the WTC and chronic post-traumatic stress disorder, including regularly re-experiencing traumatic memories of the events while awake or during sleep. Despite these findings, little is understood about the long-term effects of these physical and mental exposures on the brain health of WTC-affected individuals, and the potential for neurocognitive disorders. Here, we review the existing evidence concerning neurological outcomes in WTC-affected individuals, with the aim of contextualizing this research for policymakers, researchers and clinicians and educating WTC-affected individuals and their friends and families. We conclude by providing a rationale and recommendations for monitoring the neurological health of WTC-affected individuals.

Whole-brain morphometry in Canadian soldiers with posttraumatic stress disorder

Patients with posttraumatic stress disorder (PTSD) display several structural brain differences when compared with healthy individuals. However, findings are particularly inconsistent for soldiers with PTSD. Here, we characterized the brain morphometry of 37 soldiers from the Canadian Armed Forces with adulthood war-related PTSD using structural magnetic resonance imaging. We assessed time since trauma, as well as PTSD, depressive, and anxiety symptoms with the Modified PTSD Symptoms Scale, Beck Depression Inventory, and Beck Anxiety Inventory, respectively. Whole-brain morphometry was extracted with FreeSurfer and compared with a validated normative database of more than 2700 healthy individuals. Volume and thickness from several regions differed from the norms. Frontal regions were smaller and thinner, particularly the superior and rostral middle frontal gyri. Furthermore, smaller left rostral middle frontal gyrus, left pericalcarine cortex, and right fusiform gyrus were associated with more recent trauma. All subcortical structures were bigger, except the hippocampus. These findings suggest a particular brain morphometric signature of PTSD in soldiers. Smaller and thinner frontal and larger subcortical regions support impaired top-down and/or downregulation of emotional response in PTSD. Finally, the correlation of smaller frontal, temporal, and occipital regions with more recent trauma might inform future therapeutic approaches.

Multi-Racial Normative Data for Lobar and Subcortical Brain Volumes in Old Age: Korean and Caucasian Norms May Be Incompatible With Each Other†

Brain aging is becoming an increasingly important topic, and the norms of brain structures are essential for diagnosing neurodegenerative diseases. However, previous studies of the aging brain have mostly focused on Caucasians, not East Asians. The aim of this paper was to examine ethnic differences in the aging process of brain structures or to determine to what extent ethnicity affects the normative values of lobar and subcortical volumes in clinically normal elderly and the diagnosis in multi-racial patients with Alzheimer's disease (AD). Lobar and subcortical volumes were measured using FreeSurfer from MRI data of 1,686 normal Koreans (age range 59–89) and 851 Caucasian, non-Hispanic subjects in the ADNI and OASIS datasets. The regression models were designed to predict brain volumes, including ethnicity, age, sex, intracranial volume (ICV), magnetic field strength (MFS), and MRI scanner manufacturers as independent variables. Ethnicity had a significant effect for all lobar (|β| > 0.20, p < 0.001) and subcortical regions (|β| > 0.08, p < 0.001) except left pallidus and bilateral ventricles. To demonstrate the validity of the z-score for AD diagnosis, 420 patients and 420 normal controls were selected evenly from the Korean and Caucasian datasets. The four validation groups divided by race and diagnosis were matched on age and sex using a propensity score matching. We analyzed whether and to what extent the ethnicity adjustment improved the diagnostic power of the logistic regression model that was built using the only z-scores of six regions: bilateral temporal cortices, hippocampi, and amygdalae. The performance of the classifier after ethnicity adjustment was significantly improved compared with the classifier before ethnicity adjustment (ΔAUC = 0.10, D = 7.80, p < 0.001; AUC comparison test using bootstrap). Korean AD dementia patients may not be classified by Caucasian norms of brain volumes because the brain regions vulnerable to AD dementia are bigger in normal Korean elderly peoples. Therefore, ethnicity is an essential factor in establishing normative data for regional volumes in brain aging and applying it to the diagnosis of neurodegenerative diseases.

The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.

Multimodal neuroimaging of sex differences in cognitively impaired patients on the Alzheimer's continuum: greater tau-PET retention in females

We assessed sex differences in amyloid- and tau-PET retention in 119 amyloid positive patients with mild cognitive impairment or Alzheimer's disease (AD) dementia. Patients underwent 3T-MRI, ¹¹C-PIB amyloid-PET and ¹⁸F-Flortaucipir tau-PET. Linear ordinary least squares regression models tested sex differences in Flortaucipir-PET SUVR in a summary temporal region of interest as well as global PIB-PET. No sex differences were observed in demographics, Clinical Dementia Rating Sum of Boxes (CDR-SoB), Mini-Mental State Exam (MMSE), raw episodic memory scores, or cortical thickness. Females had higher global PIB SUVR (η_p²=.043, p=.025) and temporal Flortaucipir SUVR (η_p²=.070, p=.004), adjusting for age and CDR-SoB. Sex differences in temporal Flortaucipir-PET remained significant when controlling additionally for PIB SUVR and APOE4 status (η_p²=.055, p=.013), or when using partial volume-corrected data. No sex differences were present in areas of known Flortaucipir off-target binding. Overall, females demonstrated greater AD regional tau-PET burden than males despite clinical comparability. Further characterization of sex differences will provide insight into AD pathogenesis and support development of personalized therapeutic strategies.

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size

With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."

Clinically Applicable Quantitative Magnetic Resonance Morphologic Measurements of Grey Matter Changes in the Human Brain

(1) Purpose: Quantitative magnetic resonance imaging (qMRI) measurements can be used to sensitively estimate brain morphological alterations and may support clinical diagnosis of neurodegenerative diseases (ND). We aimed to establish a normative reference database for a clinical applicable quantitative MR morphologic measurement on neurodegenerative changes in patients; (2) Methods: Healthy subjects (HCs, n = 120) with an evenly distribution between 21 to 70 years and amyotrophic lateral sclerosis (ALS) patients (n = 11, mean age = 52.45 ± 6.80 years), as an example of ND patients, underwent magnetic resonance imaging (MRI) examinations under routine diagnostic conditions. Regional cortical thickness (rCTh) in 68 regions of interest (ROIs) and subcortical grey matter volume (SGMV) in 14 ROIs were determined from all subjects by using Computational Anatomy Toolbox. Those derived from HCs were analyzed to determine age-related differences and subsequently used as reference to estimate ALS-related alterations; (3) Results: In HCs, the rCTh (in 49/68 regions) and the SGMV (in 9/14 regions) in elderly subjects were less than those in younger subjects and exhibited negative linear correlations to age (p < 0.0007 for rCTh and p < 0.004 for SGMV). In comparison to age- and sex-matched HCs, the ALS patients revealed significant decreases of rCTh in eight ROIs, majorly located in frontal and temporal lobes; (4) Conclusion: The present study proves an overall grey matter decline with normal ageing as reported previously. The provided reference may be used for detection of grey matter alterations in neurodegenerative diseases that are not apparent in standard MR scans, indicating the potential of using qMRI as an add-on diagnostic tool in a clinical setting.

Brain Reserve in a Case of Cognitive Resilience to Severe Leukoaraiosis

OBJECTIVE

Leukoaraiosis, or white matter rarefaction, is a common imaging finding in aging and is presumed to reflect vascular disease. When severe in presentation, potential congenital or acquired etiologies are investigated, prompting referral for neuropsychological evaluation in addition to neuroimaging. T2-weighted imaging is the most common magnetic resonance imaging (MRI) approach to identifying white matter disease. However, more advanced diffusion MRI techniques may provide additional insight into mechanisms that influence the abnormal T2 signal, especially when clinical presentations are discrepant with imaging findings.

METHOD

We present a case of a 74-year-old woman with severe leukoaraoisis. She was examined by a neurologist, neuropsychologist, and rheumatologist, and completed conventional (T1, T2-FLAIR) MRI, diffusion tensor imaging (DTI), and advanced single-shell, high b-value diffusion MRI (i.e., fiber ball imaging [FBI]).

RESULTS

The patient was found to have few neurological signs, no significant cognitive impairment, a negative workup for leukoencephalopathy, and a positive antibody for Sjogren's disease for which her degree of leukoaraiosis would be highly atypical. Tractography results indicate intact axonal architecture that was better resolved using FBI rather than DTI.

CONCLUSIONS

This case illustrates exceptional cognitive resilience in the face of severe leukoaraiosis and the potential for advanced diffusion MRI to identify brain reserve.

MRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we?

The global population is aging, leading to an increasing burden of age-related neurodegenerative disease. Efforts to intervene against age-related dementias in older adults have generally proven ineffective. These failures suggest that a lifetime of brain aging may be difficult to reverse once widespread deterioration has occurred. To test interventions in younger populations, biomarkers of brain aging are needed that index subtle signs of accelerated brain deterioration that are part of the putative pathway to dementia. Here I review potential MRI-based biomarkers that could connect midlife brain aging to later life dementia. I survey the literature with three questions in mind, 1) Does the biomarker index age-related changes across the lifespan? 2) Does the biomarker index cognitive ability and cognitive decline? 3) Is the biomarker sensitive to known risk factors for dementia? I find that while there is preliminary support for some midlife MRI-based biomarkers for accelerated aging, the longitudinal research that would best answer these questions is still in its infancy and needs to be further developed. I conclude with suggestions for future research.

Interaction of Sleep and Cortical Structural Maintenance From an Individual Person Microlongitudinal Perspective and Implications for Precision Medicine Research

Sleep and maintenance of brain structure are essential for the continuity of a person's cognitive/mental health. Interestingly, whether normal structural maintenance of the brain and sleep continuously interact in some way over day-week-month times has never been assessed at an individual-person level. This study used unconventional microlongitudinal sampling, structural magnetic resonance imaging, and n-of-1 analyses to assess normal interactions between fluctuations in the structural maintenance of cerebral cortical thickness and sleep duration for day, week, and multi-week intervals over a 6-month period in a healthy adult man. Correlation and time series analyses provided indications of "if-then," i.e., "if" this preceded "then" this followed, sleep-to-thickness maintenance and thickness maintenance-to-sleep bidirectional inverse interactions. Inverse interaction patterns were characterized by concepts of graded influences across nights, bilaterally positive relationships, continuity across successive weeks, and longer delayed/prolonged effects in the thickness maintenance-to-sleep than sleep-to-thickness maintenance direction. These interactions are proposed to involve normal circadian/allostatic/homeostatic mechanisms that continuously influence, and are influenced by, cortical substrate remodeling/turnover and sleep/wake cycle. Understanding interactions of individual person "-omics" is becoming a central interest in precision medicine research. The present n-of-1 findings contribute to this interest and have implications for precision medicine research use of a person's cortical structural and sleep "-omics" to optimize the continuous maintenance of that individual's cortical structure, sleep, and cognitive/mental health.

Reduced cortical thickness in World Trade Center responders with cognitive impairment

INTRODUCTION

This study examined cortical thickness (CTX) in World Trade Center (WTC) responders with cognitive impairment (CI).

METHODS

WTC responders (N = 99) with/without CI, recruited from an epidemiologic study, completed a T1-MPRAGE protocol. CTX was automatically computed in 34 regions of interest. Region-based and surface-based morphometry examined CTX in CI versus unimpaired responders. CTX was automatically computed in 34 regions of interest. Region-based measures were also compared to published norms.

RESULTS

Participants were 55.8 (SD = 0.52) years old; 48 had CI. Compared to unimpaired responders, global mean CTX was reduced in CI and across 21/34 cortical subregions. Surface-based analyses revealed reduced CTX across frontal, temporal, and parietal lobes when adjusting for multiple comparisons. Both CI and unimpaired WTC groups had reduced CTX in the entorhinal and temporal cortices compared to published normative data.

DISCUSSION

Results from the first structural magnetic resonance imaging study in WTC responders identified reduced CTX consistent with a neurodegenerative disease of unknown etiology.

Computerized Analysis of Brain MRI Parameter Dynamics in Young Patients With Cushing Syndrome-A Case-Control Study

BACKGROUND

Young patients with Cushing Syndrome (CS) may develop cognitive and behavioral alterations during disease course.

METHODS

To investigate the effects of CS on the brain, we analyzed consecutive MRI scans of patients with (n = 29) versus without CS (n = 8). Multiple brain compartments were processed for total and gray/white matter (GM/WM) volumes and intensities, and cortical volume, thickness, and surface area. Dynamics (last/baseline scans ratio per parameter) were analyzed versus cortisol levels and CS status (persistent, resolved, and non-CS).

RESULTS

Twenty-four-hour urinary free cortisol (24hUFC) measurements had inverse correlation with the intensity of subcortical GM structures and of the corpus callosum, and with the cerebral WM intensity. 24hUFC dynamics had negative correlation with volume dynamics of multiple cerebral and cerebellar structures. Patients with persistent CS had less of an increase in cortical thickness and WM intensity, and less of a decrease in WM volume compared with patients with resolution of CS. Patients with resolution of their CS had less of an increase in subcortical GM and cerebral WM volumes, but a greater increase in cortical thickness of frontal lobe versus controls.

CONCLUSION

Changes in WM/GM consistency, intensity, and homogeneity in patients with CS may correlate with CS clinical consequences better than volume dynamics alone.

Brain structure, IQ, and psychopathology in young offspring of patients with schizophrenia or bipolar disorder

BACKGROUND

Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology.

METHODS

T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8-19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model.

RESULTS

SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d = -0.56) and Co (d = -0.59), which was largely independent of IQ (respectively, d = -0.54 and d = -0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d = -0.42) and Co (d = -0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings.

CONCLUSIONS

Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder.

Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice

OBJECTIVES

Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders.

METHODS

NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer's disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients' clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated.

RESULTS

NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively).

CONCLUSION

NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases.

KEY POINTS

• Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone. • Differential diagnosis of regionally distinct atrophy patterns is improved. • Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.

Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.

Cerebrospinal Fluid and MRI Biomarkers in Neurodegenerative Diseases: A Retrospective Memory Clinic-Based Study

BACKGROUND

Cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) biomarkers of neurodegenerative diseases are relatively sensitive and specific in highly curated research cohorts, but proper validation for clinical use is mostly missing.

OBJECTIVE

We studied these biomarkers in a novel memory clinic cohort with a variety of different neurodegenerative diseases.

METHODS

This study consisted of 191 patients with subjective or objective cognitive impairment who underwent neurological, CSF biomarker (Aβ42, p-tau, and tau) and T1-weighted MRI examinations at Kuopio University Hospital. We assessed CSF and imaging biomarkers, including structural MRI focused on volumetric and cortical thickness analyses, across groups stratified based on different clinical diagnoses, including Alzheimer's disease (AD), frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, vascular dementia, and mild cognitive impairment (MCI), and subjects with no evidence of neurodegenerative disease underlying the cognitive symptoms. Imaging biomarkers were also studied by profiling subjects according to the novel amyloid, tau, and, neurodegeneration (AT(N)) classification.

RESULTS

Numerous imaging variables differed by clinical diagnosis, including hippocampal, amygdalar and inferior lateral ventricular volumes and entorhinal, lingual, inferior parietal and isthmus cingulate cortical thicknesses, at a false discovery rate (FDR)-corrected threshold for significance (analysis of covariance; p < 0.005). In volumetric comparisons by AT(N) profile, hippocampal volume significantly differed (p < 0.001) between patients with normal AD biomarkers and patients with amyloid pathology.

CONCLUSION

Our analysis suggests that CSF and MRI biomarkers function well also in clinical practice across multiple clinical diagnostic groups in addition to AD, MCI, and cognitively normal groups.

Braak neurofibrillary tangle staging prediction from in vivo MRI metrics

INTRODUCTION

Alzheimer's disease diagnosis requires postmortem visualization of amyloid and tau deposits. As brain atrophy can provide assessment of consequent neurodegeneration, our objective was to predict postmortem neurofibrillary tangles (NFT) from in vivo MRI measurements.

METHODS

All participants with neuroimaging and neuropathological data from the Alzheimer's Disease Neuroimaging Initiative, the National Alzheimer's Coordinating Center and the Rush Memory and Aging Project were selected (n = 186). Two hundred and thirty two variables were extracted from last MRI before death using FreeSurfer. Nonparametric correlation analysis and multivariable support vector machine classification were performed to provide a predictive model of Braak NFT staging.

RESULTS

We demonstrated that 59 of our MRI variables, mostly temporal lobe structures, were significantly associated with Braak NFT stages (P < .005). We obtained a 62.4% correct classification rate for discrimination between transentorhinal, limbic, and isocortical groups.

DISCUSSION

Structural neuroimaging may therefore be considered as a potential biomarker for early detection of Alzheimer's disease-associated neurofibrillary degeneration.

Tau PET and multimodal brain imaging in patients at risk for chronic traumatic encephalopathy

OBJECTIVE

To characterize individual and group-level neuroimaging findings in patients at risk for Chronic Traumatic Encephalopathy (CTE).

METHODS

Eleven male patients meeting criteria for Traumatic Encephalopathy Syndrome (TES, median age: 64) underwent neurologic evaluation, 3-Tesla MRI, and PET with [18F]-Flortaucipir (FTP, tau-PET) and [11C]-Pittsburgh compound B (PIB, amyloid-PET). Six patients underwent [18F]-Fluorodeoxyglucose-PET (FDG, glucose metabolism). We assessed imaging findings at the individual patient level, and in group-level comparisons with modality-specific groups of cognitively normal older adults (CN). Tau-PET findings in patients with TES were also compared to a matched group of patients with mild cognitive impairment or dementia due to Alzheimer's disease (AD).

RESULTS

All patients with TES sustained repetitive head injury participating in impact sports, ten in American football. Three patients met criteria for dementia and eight had mild cognitive impairment. Two patients were amyloid-PET positive and harbored the most severe MRI atrophy, FDG hypometabolism, and FTP-tau PET binding. Among the nine amyloid-negative patients, tau-PET showed either mildly elevated frontotemporal binding, a "dot-like" pattern, or no elevated binding. Medial temporal FTP was mildly elevated in a subset of amyloid-negative patients, but values were considerably lower than in AD. Voxelwise analyses revealed a convergence of imaging abnormalities (higher FTP binding, lower FDG, lower gray matter volumes) in frontotemporal areas in TES compared to controls.

CONCLUSIONS

Mildly elevated tau-PET binding was observed in a subset of amyloid-negative patients at risk for CTE, in a distribution consistent with CTE pathology stages III-IV. FTP-PET may be useful as a biomarker of tau pathology in CTE but is unlikely to be sensitive to early disease stages.

Association of Neurocognitive and Physical Function With Gait Speed in Midlife

IMPORTANCE

Gait speed is a well-known indicator of risk of functional decline and mortality in older adults, but little is known about the factors associated with gait speed earlier in life.

OBJECTIVES

To test the hypothesis that slow gait speed reflects accelerated biological aging at midlife, as well as poor neurocognitive functioning in childhood and cognitive decline from childhood to midlife.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study uses data from the Dunedin Multidisciplinary Health and Development Study, a population-based study of a representative 1972 to 1973 birth cohort in New Zealand that observed participants to age 45 years (until April 2019). Data analysis was performed from April to June 2019.

EXPOSURES

Childhood neurocognitive functions and accelerated aging, brain structure, and concurrent physical and cognitive functions in adulthood.

MAIN OUTCOMES AND MEASURES

Gait speed at age 45 years, measured under 3 walking conditions: usual, dual task, and maximum gait speeds.

RESULTS

Of the 1037 original participants (91% of eligible births; 535 [51.6%] male), 997 were alive at age 45 years, of whom 904 (90.7%) had gait speed measured (455 [50.3%] male; 93% white). The mean (SD) gait speeds were 1.30 (0.17) m/s for usual gait, 1.16 (0.23) m/s for dual task gait, and 1.99 (0.29) m/s for maximum gait. Adults with more physical limitations (standardized regression coefficient [β], -0.27; 95% CI, -0.34 to -0.21; P < .001), poorer physical functions (ie, weak grip strength [β, 0.36; 95% CI, 0.25 to 0.46], poor balance [β, 0.28; 95% CI, 0.21 to 0.34], poor visual-motor coordination [β, 0.24; 95% CI, 0.17 to 0.30], and poor performance on the chair-stand [β, 0.34; 95% CI, 0.27 to 0.40] or 2-minute step tests [β, 0.33; 95% CI, 0.27 to 0.39]; all P < .001), accelerated biological aging across multiple organ systems (β, -0.33; 95% CI, -0.40 to -0.27; P < .001), older facial appearance (β, -0.25; 95% CI, -0.31 to -0.18; P < .001), smaller brain volume (β, 0.15; 95% CI, 0.06 to 0.23; P < .001), more cortical thinning (β, 0.09; 95% CI, 0.02 to 0.16; P = .01), smaller cortical surface area (β, 0.13; 95% CI, 0.04 to 0.21; P = .003), and more white matter hyperintensities (β, -0.09; 95% CI, -0.15 to -0.02; P = .01) had slower gait speed. Participants with lower IQ in midlife (β, 0.38; 95% CI, 0.32 to 0.44; P < .001) and participants who exhibited cognitive decline from childhood to adulthood (β, 0.10; 95% CI, 0.04 to 0.17; P < .001) had slower gait at age 45 years. Those with poor neurocognitive functioning as early as age 3 years had slower gait in midlife (β, 0.26; 95% CI, 0.20 to 0.32; P < .001).

CONCLUSIONS AND RELEVANCE

Adults' gait speed is associated with more than geriatric functional status; it is also associated with midlife aging and lifelong brain health.

Progressive Cortical Thinning in Patients With Focal Epilepsy

Importance

It is controversial whether epilepsy is a static or progressive disease. Evidence of progressive gray matter loss in epilepsy would support early diagnosis, rapid treatment, and early referral for surgical interventions.

Objective

To demonstrate progressive cortical thinning in patients with focal epilepsy distinct from cortical thinning associated with normal aging.

Design, Setting, and Participants

A case-control neuroimaging study was conducted from August 3, 2004, to January 26, 2016, among 190 patients with focal epilepsy at a tertiary epilepsy referral center (epilepsy data) and 3 independent comparison cohorts matched for age and sex (healthy volunteer data; n = 141).

Exposures

Two or more high-resolution T1-weighted magnetic resonance imaging scans at least 6 months apart (mean [SD] interval, 2.5 [1.6] years).

Main Outcomes and Measures

Global and vertexwise rate of progressive cortical thinning.

Results

A total of 190 people with focal epilepsy (99 women and 91 men; mean [SD] age, 36 [11] years; 396 magnetic resonance imaging scans) were compared with 141 healthy volunteers (76 women and 65 men; mean [SD] age, 35 [17] years; 282 magnetic resonance imaging scans). Widespread highly significant progressive cortical thinning exceeding normal aging effects, mainly involving the bilateral temporal lobes, medial parietal and occipital cortices, pericentral gyri, and opercula, was seen in 146 individuals with epilepsy (76.8%; 95% CI, 58%-95%). The mean (SD) annualized rate of global cortical thinning in patients with epilepsy was twice the rate of age-associated thinning observed in healthy volunteers (0.024 [0.061] vs 0.011 [0.029] mm/y; P = .01). Progression was most pronounced in adults older than 55 years and during the first 5 years after the onset of seizures. Areas of accelerated cortical thinning were detected in patients with early onset of epilepsy and in patients with hippocampal sclerosis. Accelerated thinning was not associated with seizure frequency, history of generalized seizures, or antiepileptic drug load and did not differ between patients with or without ongoing seizures. Progressive atrophy in temporal (n = 101) and frontal (n = 28) lobe epilepsy was most pronounced ipsilaterally to the epileptic focus but also affected a widespread area extending beyond the focus and commonly affected the contralateral hemisphere. For patients with temporal lobe epilepsy, accelerated cortical thinning was observed within areas structurally connected with the ipsilateral hippocampus.

Conclusions and Relevance

Widespread progressive cortical thinning exceeding that seen with normal aging may occur in patients with focal epilepsy. These findings appear to highlight the need to develop epilepsy disease-modifying treatments to disrupt or slow ongoing atrophy. Longitudinal cortical thickness measurements may have the potential to serve as biomarkers for such studies.

Brain atrophy and patch-based grading in individuals from the CIMA-Q study: a progressive continuum from subjective cognitive decline to AD

It has been proposed that individuals developing Alzheimer's disease (AD) first experience a phase expressing subjective complaints of cognitive decline (SCD) without objective cognitive impairment. Using magnetic resonance imaging (MRI), our objective was to verify whether SNIPE probability grading, a new MRI analysis technique, would distinguish between clinical dementia stage of AD: Cognitively healthy controls without complaint (CH), SCD, mild cognitive impairment, and AD. SNIPE score in the hippocampus and entorhinal cortex was applied to anatomical T1-weighted MRI of 143 participants from the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) study and compared to standard atrophy measures (volumes and cortical thicknesses). Compared to standard atrophy measures, SNIPE score appeared more sensitive to differentiate clinical AD since differences between groups reached a higher level of significance and larger effect sizes. However, no significant difference was observed between SCD and CH groups. Combining both types of measures did not improve between-group differences. Further studies using a combination of biomarkers beyond anatomical MRI might be needed to identify individuals with SCD who are on the beginning of the clinical continuum of AD.

Measurement Variability Following MRI System Upgrade

Major hardware/software changes to MRI platforms, either planned or unplanned, will almost invariably occur in longitudinal studies. Our objective was to assess the resulting variability on relevant imaging measurements in such context, specifically for three Siemens Healthcare Magnetom Trio upgrades to the Prisma^fit platform. We report data acquired on three healthy volunteers scanned before and after three different platform upgrades. We assessed differences in image signal [contrast-to-noise ratio (CNR)] on T1-weighted images (T1w) and fluid-attenuated inversion recovery images (FLAIR); brain morphometry on T1w image; and small vessel disease (white matter hyperintensities; WMH) on FLAIR image. Prisma^fit upgrade resulted in higher (30%) and more variable neocortical CNR and larger brain volume and thickness mainly in frontal areas. A significant relationship was observed between neocortical CNR and neocortical volume. For FLAIR images, no significant CNR difference was observed, but WMH volumes were significantly smaller (-68%) after Prisma^fit upgrade, when compared to results on the Magnetom Trio. Together, these results indicate that Prisma^fit upgrade significantly influenced image signal, brain morphometry measures and small vessel diseases measures and that these effects need to be taken into account when analyzing results from any longitudinal study undergoing similar changes.

Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability

Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.

Interdatabase Variability in Cortical Thickness Measurements

The phenomenon of cortical thinning with age has been well established; however, the measured rate of change varies between studies. The source of this variation could be image acquisition techniques including hardware and vendor specific differences. Databases are often consolidated to increase the number of subjects but underlying differences between these datasets could have undesired effects. We explore differences in cerebral cortex thinning between 4 databases, totaling 1382 subjects. We investigate several aspects of these databases, including: 1) differences between databases of cortical thinning rates versus age, 2) correlation of cortical thinning rates between regions for each database, and 3) regression bootstrapping to determine the effect of the number of subjects included. We also examined the effect of different databases on age prediction modeling. Cortical thinning rates were significantly different between databases in all 68 parcellated regions (ANCOVA, P < 0.001). Subtle differences were observed in correlation matrices and bootstrapping convergence. Age prediction modeling using a leave-one-out cross-validation approach showed varying prediction performance (0.64 < R2 < 0.82) between databases. When a database was used to calibrate the model and then applied to another database, prediction performance consistently decreased. We conclude that there are indeed differences in the measured cortical thinning rates between these large-scale databases.