Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast

Machine Learning-Driven Prediction of Brain Age for Alzheimer's Risk: APOE4 Genotype and Gender Effects

Background: Alzheimer's disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 patients with AD. We applied three ML regression models-XGBoost, random forest (RF), and linear regression (LR)-to predict brain age. Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models' performances and analyze the influences of the APOE4 genotype and gender on brain aging. Results: Patients with AD displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. Gender-specific analyses indicated slight enhancements, with the models performing equally well for both genders. Conclusions: This study demonstrates that robust ML models for brain age prediction can play a crucial role in the early detection of AD risk through MRI brain structural imaging. The significant impact of the APOE4 genotype on brain aging and AD risk is also emphasized. These findings highlight the potential of ML models in assessing AD risk and suggest that utilizing AI for AD identification could enable earlier preventative interventions.

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.

Brain structural indicators of β-amyloid neuropathology

Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.

Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change

Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.

Explainable machine learning radiomics model for Primary Progressive Aphasia classification

Introduction

Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants. Diagnosing and classifying PPA patients represents a complex challenge that requires the integration of multimodal information, including clinical, biological, and radiological features. Structural neuroimaging can play a crucial role in aiding the differential diagnosis of PPA and constructing diagnostic support systems.

Methods

In this study, we conducted a white matter texture analysis on T1-weighted images, including 56 patients with PPA (31 svPPA and 25 nfvPPA), and 53 age- and sex-matched controls. We trained a tree-based algorithm over combined clinical/radiomics measures and used Shapley Additive Explanations (SHAP) model to extract the greater impactful measures in distinguishing svPPA and nfvPPA patients from controls and each other.

Results

Radiomics-integrated classification models demonstrated an accuracy of 95% in distinguishing svPPA patients from controls and of 93.7% in distinguishing svPPA from nfvPPA. An accuracy of 93.7% was observed in differentiating nfvPPA patients from controls. Moreover, Shapley values showed the strong involvement of the white matter near left entorhinal cortex in patients classification models.

Discussion

Our study provides new evidence for the usefulness of radiomics features in classifying patients with svPPA and nfvPPA, demonstrating the effectiveness of an explainable machine learning approach in extracting the most impactful features for assessing PPA.

Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years

Background

A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth.

Methods

To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology.

Results

Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC.

Conclusion

Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.

Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters

Volumetric assessment based on structural MRI is increasingly recognized as an auxiliary tool to visual reading, also in examinations acquired in the clinical routine. However, MRI acquisition parameters can significantly influence these measures, which must be considered when interpreting the results on an individual patient level. This Technical Note shall demonstrate the problem. Using data from a dedicated experiment, we show the influence of two crucial sequence parameters on the GM/WM contrast and their impact on the measured volumes. A simulated contrast derived from acquisition parameters TI/TR may serve as surrogate and is highly correlated (r=0.96) with the measured contrast.

The relationship between gray/white matter contrast and cognitive performance in first-episode schizophrenia

Previous postmortem brain studies have revealed disturbed myelination in the intracortical regions in patients with schizophrenia, possibly reflecting anomalous brain maturational processes. However, it currently remains unclear whether this anomalous myelination is already present in early illness stages and/or progresses during the course of the illness. In this magnetic resonance imaging study, we examined gray/white matter contrast (GWC) as a potential marker of intracortical myelination in 63 first-episode schizophrenia (FESz) patients and 77 healthy controls (HC). Furthermore, we investigated the relationships between GWC findings and clinical/cognitive variables in FESz patients. GWC in the bilateral temporal, parietal, occipital, and insular regions was significantly higher in FESz patients than in HC, which was partly associated with the durations of illness and medication, the onset age, and lower executive and verbal learning performances. Because higher GWC implicates lower myelin in the deeper layers of the cortex, these results suggest that schizophrenia patients have less intracortical myelin at the time of their first psychotic episode, which underlies lower cognitive performance in early illness stages.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure-function coupling as well as the network vulnerability to damage to rich club connectivity. Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure-function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.

Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β-amyloid in dementia

Cortical gray to white matter signal intensity ratio (GWR) measured from T1-weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross-sectional dataset and a longitudinal dataset. The cross-sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross-sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β-amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β-amyloid deposition, MCI without β-amyloid deposition, and controls. The spatial pattern of GWR-increased regions was not influenced by β-amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β-amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR-increased regions was independent of β-amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.

Gray-white matter boundary Z-score and volume as imaging biomarkers of Alzheimer's disease

Introduction

Alzheimer's disease (AD) presents typically gray matter atrophy and white matter abnormalities in neuroimaging, suggesting that the gray-white matter boundary could be altered in individuals with AD. The purpose of this study was to explore differences of gray-white matter boundary Z-score (gwBZ) and its tissue volume (gwBTV) between patients with AD, amnestic mild cognitive impairment (MCI), and cognitively normal (CN) elderly participants.

Methods

Three-dimensional T1-weight images of a total of 227 participants were prospectively obtained from our institute from 2006 to 2022 to map gwBZ and gwBTV on images. Statistical analyses of gwBZ and gwBTV were performed to compare the three groups (AD, MCI, CN), to assess their correlations with age and Korean version of the Mini-Mental State Examination (K-MMSE), and to evaluate their effects on AD classification in the hippocampus.

Results

This study included 62 CN participants (71.8 ± 4.8 years, 20 males, 42 females), 72 MCI participants (72.6 ± 5.1 years, 23 males, 49 females), and 93 AD participants (73.6 ± 7.7 years, 22 males, 71 females). The AD group had lower gwBZ and gwBTV than CN and MCI groups. K-MMSE showed positive correlations with gwBZ and gwBTV whereas age showed negative correlations with gwBZ and gwBTV. The combination of gwBZ or gwBTV with K-MMSE had a high accuracy in classifying AD from CN in the hippocampus with an area under curve (AUC) value of 0.972 for both.

Conclusion

gwBZ and gwBTV were reduced in AD. They were correlated with cognitive function and age. Moreover, gwBZ or gwBTV combined with K-MMSE had a high accuracy in differentiating AD from CN in the hippocampus. These findings suggest that evaluating gwBZ and gwBTV in AD brain could be a useful tool for monitoring AD progression and diagnosis.

Altered gray-to-white matter tissue contrast in preterm-born adults

AIMS

To investigate cortical organization in brain magnetic resonance imaging (MRI) of preterm-born adults using percent contrast of gray-to-white matter signal intensities (GWPC), which is an in vivo proxy measure for cortical microstructure.

METHODS

Using structural MRI, we analyzed GWPC at different percentile fractions across the cortex (0%, 10%, 20%, 30%, 40%, 50%, and 60%) in a large and prospectively collected cohort of 86 very preterm-born (<32 weeks of gestation and/or birth weight <1500 g, VP/VLBW) adults and 103 full-term controls at 26 years of age. Cognitive performance was assessed by full-scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale.

RESULTS

GWPC was significantly decreased in VP/VLBW adults in frontal, parietal, and temporal associative cortices, predominantly in the right hemisphere. Differences were pronounced at 20%, 30%, and 40%, hence, in middle cortical layers. GWPC was significantly increased in right paracentral lobule in VP/VLBW adults. GWPC in frontal and temporal cortices was positively correlated with birth weight, and negatively with duration of ventilation (p < 0.05). Furthermore, GWPC in right paracentral lobule was negatively correlated with IQ (p < 0.05).

CONCLUSIONS

Widespread aberrant gray-to-white matter contrast suggests lastingly altered cortical microstructure after preterm birth, mainly in middle cortical layers, with differential effects on associative and primary cortices.

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders.

Increased MRI-based Brain Age in chronic migraine patients

INTRODUCTION

Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.

METHODS

We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).

RESULTS

CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.

CONCLUSION

The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

Profiling intra- and inter-individual differences in brain development across early adolescence

As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.

Spatio-temporal evolution of human neural activity during visually cued hand movements

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review

Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

Objective

To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.

Methods

Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.

Results

We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.

Conclusion

These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.

T1 relaxation time is prolonged in healthy aging: a whole brain study

BACKGROUND

: Measurement of tissue characteristics such as the longitudinal relaxation time (T1) provides complementary information to the volumetric and surface based structural analyses. We aimed to investigate T1 relaxation time characteristics in healthy aging via an exploratory design in the whole brain. The data processing pipeline was designed to minimize errors related to aging effects such as atrophy.

METHODS

Sixty healthy participants underwent MRI scanning (28 F, 32 M, age range: 18-78, 30 young and 30 old) in November 2017-March 2018 at the Bilkent University UMRAM Center. Four images with varying flip angles with FLASH (fast low angle shot magnetic resonance imaging) sequence and a high-resolution structural image with MP-RAGE (Magnetization Prepared - RApid Gradient Echo) were acquired. T1 relaxation times of the entire brain were mapped by using the region of interest (ROI) based method on 134 brain areas in young and old populations.

RESULTS

T1 prolongation was observed in various subcortical (bilateral hippocampus, caudate and thalamus) and cortical brain structures (bilateral precentral gyrus, bilateral middle frontal gyrus, bilateral supplementary motor area (SMA), left middle occipital gyrus, bilateral postcentral gyrus and bilateral Heschl's gyrus) as well as cerebellar regions (GM regions of cerebellum: bilateral cerebellum III, cerebellum IV V, cerebellum X, cerebellar vermis u 4 5, cerebellar vermis u 9 and WM cerebellar regions: left cerebellum IX, bilateral cerebellum X and cerebellar vermis u 4 5).

DISCUSSION

T1 mapping provides a practical quantitative MRI (qMRI) methodology for studying the tissue characteristics in healthy aging. T1 values are significantly increased in the aging group among half of the studied ROIs (57 ROIs out of 134).

Telomere length and brain imaging phenotypes in UK Biobank

Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.

Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia

Introduction

Primary Progressive Aphasia (PPA) is a neurological disease characterized by linguistic deficits. Semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants are the two main clinical subtypes. We applied a novel analytical framework, based on radiomic analysis, to investigate White Matter (WM) asymmetry and to examine whether asymmetry is associated with verbal fluency performance.

Methods

Analyses were performed on T1-weighted images including 56 patients with PPA (31 svPPA and 25 nfvPPA) and 53 age- and sex-matched controls. Asymmetry Index (AI) was computed for 86 radiomics features in 34 white matter regions. The relationships between AI, verbal fluency performance (semantic and phonemic) and Boston Naming Test score (BNT) were explored through Spearman correlation analysis.

Results

Relative to controls, WM asymmetry in svPPA patients involved regions adjacent to middle temporal cortex as part of the inferior longitudinal (ILF), fronto-occipital (IFOF) and superior longitudinal fasciculi. Conversely, nfvPPA patients showed an asymmetry of WM in lateral occipital regions (ILF/IFOF). A higher lateralization involving IFOF, cingulum and forceps minor was found in nfvPPA compared to svPPA patients. In nfvPPA patients, semantic fluency was positively correlated to asymmetry in ILF/IFOF tracts. Performances at BNT were associated with AI values of the middle temporal (ILF/SLF) and parahippocampal (ILF/IFOF) gyri in svPPA patients.

Discussion

Radiomics features depicted distinct pathways of asymmetry in svPPA and nfvPPA involving damage of principal fiber tracts associated with speech and language. Assessing asymmetry of radiomics in PPA allows achieving a deeper insight into the neuroanatomical damage and may represent a candidate severity marker for language impairments in PPA patients.

Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients

OBJECTIVES AND AIMS

Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice.

METHODS

We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS).

RESULTS

The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R² = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R² = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007).

CONCLUSIONS

We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.

Gray to white matter signal ratio as a novel biomarker of neurodegeneration in Alzheimer's disease

Alzheimer's disease (AD) is characterized neuropathologically by β-amyloid (Aβ) plaques, hyperphosphorylated tau neurofibrillary tangles, and neurodegeneration, which lead to a phenotypically heterogeneous cognitive-behavioral dementia syndrome. Our understanding of how these neuropathological and neurodegeneration biomarkers relate to each other is still evolving. A relatively new approach to measuring structural brain change, gray matter to white matter signal intensity ratio (GWR), quantifies the signal contrast between these tissue compartments, and has emerged as a promising marker of AD-related neurodegeneration. We sought to validate GWR as a novel MRI biomarker of neurodegeneration in 29 biomarker positive individuals across the atypical syndromic spectrum of AD. Bivariate correlation analyses revealed that GWR was associated with cortical thickness, tau PET, and amyloid PET, with GWR showing a larger magnitude of abnormality than cortical thickness. We also found that combining GWR, cortical thickness, and amyloid PET better explained observed tau PET signal than using these modalities alone, suggesting that the three imaging biomarkers contribute independently and synergistically to explaining the variance in the distribution of tau pathology. We conclude that GWR is a uniquely sensitive in vivo marker of neurodegenerative change that reflects pathological mechanisms which may occur prior to cortical atrophy. By using all of these imaging biomarkers of AD together, we may be better able to capture, and possibly predict, AD neuropathologic changes in vivo. We hope that such an approach will ultimately contribute to better endpoints to evaluate the efficacy of therapeutic interventions as we move toward an era of disease-modifying treatments for this devastating disease.

Investigation of endophenotype potential of decreased fractional anisotropy in pediatric bipolar disorder patients and unrelated offspring of bipolar disorder patients

BACKGROUND

Bipolar disorder (BD) is a severe psychiatric disorder associated with structural and functional brain abnormalities, some of which have been found in unaffected relatives as well. In this study, we examined the potential role of decreased fractional anisotropy (FA) as a BD endophenotype, in adolescents at high risk for BD.

METHODS

We included 15 offspring of patients with BD, 16 pediatric BD patients, and 16 matched controls. Diffusion weighted scans were obtained on a 3T scanner using an echo-planar sequence. Scans were segmented using FreeSurfer.

RESULTS

Our results showed significantly decreased FA in six brain areas of offspring group; left superior temporal gyrus (LSTG; P < .0001), left transverse temporal gyrus (LTTG; P = .002), left banks of the superior temporal sulcus (LBSTS; P = .002), left anterior cingulum (LAC; P = .003), right temporal pole (RTP; P = .004) and left frontal pole (LFP; P = .017). On analysis, LSTG, LAC, and RTP demonstrated a potential to be an endophenotype when comparing all three groups. FA values in three regions, LBSTS, LTTG, and LFP were increased only in controls.

CONCLUSION

Our findings point at decreased FA as a possible endophenotype for BD, as they were found in children of patients with BD. Most of these areas were previously found to have morphological and functional changes in adult and pediatric BD, and are thought to play important roles in affected domains of functioning. Prospective follow up studies should be performed to detect reliability of decreased FA as an endophenotype and effects of treatment on FA.

Gradual changes in microarchitectural properties of cortex and juxtacortical white matter: Observed by anatomical and diffusion MRI

PURPOSE

Characterization of cerebral cortex is challenged by the complexity and heterogeneity of its cyto- and myeloarchitecture. This study evaluates quantitative MRI metrics, measured across two cortical depths and in subcortical white matter (WM) adjacent to cortex (juxtacortical WM), indicative of myelin content, neurite density, and diffusion microenvironment, for a comprehensive characterization of cortical microarchitecture.

METHODS

High-quality structural and diffusion MRI data (N = 30) from the Human Connectome Project were processed to compute myelin index, neurite density index, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from superficial cortex, deep cortex, and juxtacortical WM. The distributional patterns of these metrics were analyzed individually, correlated to one another, and were compared to established parcellations.

RESULTS

Our results supported that myeloarchitectonic and the coexisting cytoarchitectonic structures influence the diffusion properties of water molecules residing in cortex. Full cortical thickness showed myelination patterns similar to those previously observed in humans. Higher myelin indices with similar distributional patterns were observed in deep cortex whereas lower myelin indices were observed in superficial cortex. Neurite density index and other diffusion MRI derived parameters provided complementary information to myelination. Reliable and reproducible correlations were identified among the cortical microarchitectural properties and fiber distributional patterns in proximal WM structures.

CONCLUSION

We demonstrated gradual changes across the cortical sheath by assessing depth-specific cortical micro-architecture using anatomical and diffusion MRI. Mutually independent but coexisting features of cortical layers and juxtacortical WM provided new insights towards structural organizational units and variabilities across cortical regions and through depth.

Comparison of machine learning methods for the detection of focal cortical dysplasia lesions: decision tree, support vector machine and artificial neural network

BACKGROUND

Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem. Among the new techniques for FCD lesion diagnosis, classification techniques can be of great help in FCD patient's detection from healthy individuals.

METHODS

MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated.

RESULTS

Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 90%, 100% and 95.8%, respectively; it was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN.

CONCLUSION

Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Parental socioeconomic status is linked to cortical microstructure and language abilities in children and adolescents

Gradients in parental socioeconomic status (SES) are closely linked to important life outcomes in children and adolescents, such as cognitive abilities, school achievement, and mental health. Parental SES may also influence brain development, with several magnetic resonance imaging (MRI) studies reporting associations with youth brain morphometry. However, MRI signal intensity metrics have not been assessed, but could offer a microstructural correlate, thereby increasing our understanding of SES influences on neurobiology. We computed a parental SES score from family income, parental education and parental occupation, and assessed relations with cortical microstructure as measured by T1w/T2w ratio (n = 504, age = 3-21 years). We found negative age-stabile relations between parental SES and T1w/T2w ratio, indicating that youths from lower SES families have higher ratio in widespread frontal, temporal, medial parietal and occipital regions, possibly indicating a more developed cortex. Effect sizes were small, but larger than for conventional morphometric properties i.e. cortical surface area and thickness, which were not significantly associated with parental SES. Youths from lower SES families had poorer language related abilities, but microstructural differences did not mediate these relations. T1w/T2w ratio appears to be a sensitive imaging marker for further exploring the association between parental SES and child brain development.

Bridging patterns of neurocognitive aging across the older adult lifespan

Magnetic resonance imaging (MRI) studies of brain and neurocognitive aging rarely include oldest-old adults (ages 80 +). But predictions of neurocognitive aging theories derived from MRI findings in younger-old adults (ages ~55-80) may not generalize into advanced age, particularly given the increased prevalence of cognitive impairment/dementia in the oldest-old. Here, we reviewed the MRI literature in oldest-old adults and interpreted findings within the context of regional variation, compensation, brain maintenance, and reserve theories. Structural MRI studies revealed regional variation in brain aging as larger age effects on medial temporal and posterior regions for oldest-old than younger-old adults. They also revealed that brain maintenance explained preserved cognitive functioning into the tenth decade of life. Very few functional MRI studies examined compensatory activity in oldest-old adults who perform as well as younger groups, although there was evidence that higher brain reserve in oldest-old adults may mediate effects of brain aging on cognition. Despite some continuity, different cognitive and neural profiles across the older adult lifespan should be addressed in modern neurocognitive aging theories.

Quantification of brain age using high-resolution 7 tesla MR imaging and implications for patients with epilepsy

Purpose

Epilepsy patients exhibit morphological differences on neuroimaging compared to age-matched healthy controls, including cortical and sub-cortical volume loss and altered gray-white matter ratios. The objective was to develop a model of normal aging using the 7T MRIs of healthy controls. This model can then be used to determine if the changes in epilepsy patients resemble the changes seen in aging, and potentially give a marker for the severity of those changes.

Methods

Sixty-nine healthy controls (24F/45M, mean age 36.5 ± 10.5 years) and forty-four epilepsy patients (24F/20M, 33.2 ± 9.9 years) non-lesional at 3T were scanned with volumetric T1-MPRAGE at 7T. These images were segmented and quantified using FreeSurfer. A linear regression-based model trained on healthy controls was developed to predict ages using derived imaging features among the epilepsy patient cohort. The model used 114 features with significant linear correlation with age.

Results

The regression-based model estimated brain age with mean absolute error (MAE) of 6.6 years among controls. Comparable prediction accuracy of 6.9 years MAE was seen epilepsy patients. T-test of mean absolute error showed no difference in the prediction accuracy with controls and epilepsy patients (p = 0.68). However, average signed error showed elevated (+5.0 years, p = 0.0007) predicted age differences (PAD; brain-PAD=, predicted minus biological age) among epilepsy patients. Morphological metrics in the medial temporal lobe were major contributors to PAD. Additionally, patients with seizure frequency greater than once a week showed significantly elevated brain-PAD (+8.2 ± 5.3 years, n = 13) compared to patients with lower seizure frequency (3.7 ± 6.5 years, n = 31, p = 0.033).

Major conclusions

Morphological patterns suggestive of premature aging were observed in non-lesional epilepsy patients vs. controls and in high seizure frequency patients vs. low frequency patients. Modeling brain age with 7T MRI may provide a sensitive imaging marker to assess the differential effects of the aging process in diseases such as epilepsy.

Multiscale structural mapping of Alzheimer's disease neurodegeneration

The recently described biological framework of Alzheimer's disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the 'amyloid/tau/neurodegeneration' (A-T-N) status. The 'neurodegenerative' component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the 'cortical signature of AD' provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as 'multi-scale structural mapping'; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.

Is Old Age Just a Number in Language Skills? Language Performance and Its Relation to Age, Education, Gender, Cognitive Screening, and Dentition in Very Old Finnish Speakers

PURPOSE

This study reports on how very old (VO) Finnish people without dementia perform in the Western Aphasia Battery (WAB) and two verbal fluency tasks and which demographic factors predict the performance.

METHOD

The study included fifty 80- to 100-year-old community-dwelling Finnish speakers with no dementing illnesses or speech-language disabilities, who completed the WAB and two verbal fluency tasks. Multifactorial statistical analyses with recursive partitioning were carried out to determine the significant predictors out of five predictor variables (age, gender, education, dentition, and Mini-Mental State Examination [MMSE]) for four response variables (WAB Aphasia Quotient [AQ], Language Quotient [LQ], semantic, and phonemic word fluencies).

RESULTS

Overall, individual variation was notable in VO speakers. All predictor variables were statistically significantly associated with one or more of the language skills. Age was the most significant predictor; the critical age of 85-86 years was associated with a decline in WAB-AQ and semantic fluency. Poor dentition and the MMSE score both predicted a decline in WAB-LQ and phonemic fluency. A high level of education was positively associated with the skills of the best-performing individuals in WAB-AQ, WAB-LQ, and semantic fluency.

CONCLUSIONS

VO age is a significant factor contributing to language performance. However, a younger age, a good cognitive performance, intact teeth, and a higher educational level also seem to have a preservative power as regards language skills. Gender differences should be interpreted with caution. The results of this study provide culture- and language-specific normative data, which aids in differentiating typical aging from the signs of acute or degenerative neuropathology to ensure appropriate medical and therapeutic interventions.

Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort

Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.

A sub+cortical fMRI-based surface parcellation

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.

Recycling diagnostic MRI for empowering brain morphometric research - Critical & practical assessment on learning-based image super-resolution

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.

Maturational trajectories of pericortical contrast in typical brain development

In the last few years, a significant amount of work has aimed to characterize maturational trajectories of cortical development. The role of pericortical microstructure putatively characterized as the gray-white matter contrast (GWC) at the pericortical gray-white matter boundary and its relationship to more traditional morphological measures of cortical morphometry has emerged as a means to examine finer grained neuroanatomical underpinnings of cortical changes. In this work, we characterize the GWC developmental trajectories in a representative sample (n = 394) of children and adolescents (~4 to ~22 years of age), with repeated scans (1-3 scans per subject, total scans n = 819). We tested whether linear, quadratic, or cubic trajectories of contrast development best described changes in GWC. A best-fit model was identified vertex-wise across the whole cortex via the Akaike Information Criterion (AIC). GWC across nearly the whole brain was found to significantly change with age. Cubic trajectories were likeliest for 63% of vertices, quadratic trajectories were likeliest for 20% of vertices, and linear trajectories were likeliest for 16% of vertices. A main effect of sex was observed in some regions, where males had a higher GWC than females. However, no sex by age interactions were found on GWC. In summary, our results suggest a progressive decrease in GWC at the pericortical boundary throughout childhood and adolescence. This work contributes to efforts seeking to characterize typical, healthy brain development and, by extension, can help elucidate aberrant developmental trajectories.

New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings

Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.

Altered Gray-White Matter Boundary Contrast in Toddlers at Risk for Autism Relates to Later Diagnosis of Autism Spectrum Disorder

BACKGROUND

Recent neuroimaging studies have highlighted differences in cerebral maturation in individuals with autism spectrum disorder (ASD) in comparison to typical development. For instance, the contrast of the gray-white matter boundary is decreased in adults with ASD. To determine how gray-white matter boundary integrity relates to early ASD phenotypes, we used a regional structural MRI index of gray-white matter contrast (GWC) on a sample of toddlers with a hereditary high risk for ASD.

MATERIALS AND METHODS

We used a surface-based approach to compute vertex-wise GWC in a longitudinal cohort of toddlers at high-risk for ASD imaged twice between 12 and 24 months (n = 20). A full clinical assessment of ASD-related symptoms was performed in conjunction with imaging and again at 3 years of age for diagnostic outcome. Three outcome groups were defined (ASD, n = 9; typical development, n = 8; non-typical development, n = 3).

RESULTS

ASD diagnostic outcome at age 3 was associated with widespread increases in GWC between age 12 and 24 months. Many cortical regions were affected, including regions implicated in social processing and language acquisition. In parallel, we found that early onset of ASD symptoms (i.e., prior to 18-months) was specifically associated with slower GWC rates of change during the second year of life. These alterations were found in areas mainly belonging to the central executive network.

LIMITATIONS

Our study is the first to measure maturational changes in GWC in toddlers who developed autism, but given the limited size of our sample results should be considered exploratory and warrant further replication in independent and larger samples.

CONCLUSION

These preliminary results suggest that ASD is linked to early alterations of the gray-white matter boundary in widespread brain regions. Early onset of ASD diagnosis constitutes an independent clinical parameter associated with a specific corresponding neurobiological developmental trajectory. Altered neural migration and/or altered myelination processes potentially explain these findings.

Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.

Neurodegeneration, Alzheimer's disease biomarkers, and longitudinal verbal learning and memory performance in late middle age

This study examined the effect of neurodegeneration, and its interaction with Alzheimer's disease (AD) cerebrospinal fluid biomarkers, on longitudinal verbal learning and memory performance in cognitively unimpaired (CU) late middle-aged adults. Three hundred and forty-two CU adults (cognitive baseline mean age = 58.4), with cerebrospinal fluid and structural MRI, completed 2-10 (median = 5) cognitive assessments. Learning and memory were assessed using the Rey Auditory Verbal Learning Test (RAVLT). We used sequential comparison of nested linear mixed effects models to analyze the data. Model selection preserved a significant ptau181/Aβ42 × global atrophy × age interaction; individuals with less global atrophy and lower ptau181/Aβ42 levels had less learning and delayed recall decline than individuals with more global atrophy and/or higher levels of ptau181/Aβ42. The hippocampal volume × age × ptau181/Aβ42 interaction was not significant. Findings suggest that in a sample of CU late middle-aged adults, individuals with AD biomarkers, global atrophy, or both evidence greater verbal learning and memory decline than individuals without either risk factor.

Geometric deep learning on brain shape predicts sex and age

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.

Localization of the central sulcus using the distinctive high signal intensity of the paracentral lobule on T1-weighted images

PURPOSE

The central sulcus is an important landmark in the brain. This study aimed to investigate the distinctive signal of the paracentral lobule (PL) on T1-weighted images (T1WIs; the white PL sign) and evaluate its usefulness as a new method of identifying the central sulcus.

METHODS

T1WIs of the brain of 96 participants (age, 58.9 ± 17.9 years; range, 8-87 years) scanned at 3-T MR system were retrospectively reviewed. First, we qualitatively analyzed the signal of the cortex of the PL by comparing it with that of the ipsilateral superior frontal gyrus on a 4-point grading score. Second, we compared the cortical signal intensity and gray/white-matter contrast between the PL and superior frontal gyrus. Third, we evaluated the usefulness of the PL signal for identifying the central sulcus.

RESULTS

The PL cortex was either mildly hyperintense (grade 2) or definitely hyperintense (grade 3) in comparison with that of superior frontal cortex in all participants. The signal intensity of the PL cortex was significantly higher than that of the superior frontal cortex (p < 0.001), whereas the gray/white-matter contrast of the PL was weaker than that of the superior frontal gyrus (p < 0.001). The central sulci were identified with 94.3% accuracy (181/192) using the new method.

CONCLUSION

The white PL sign may be helpful in identifying the central sulcus, and this approach can be recognized as a new method for identification of the central sulcus.

An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank

UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer's disease and mitochondrial disorders.

Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions

We aimed to develop an efficient and objective pre-evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post-operative frequency of seizure attacks. We developed a novel machine learning-based approach using cortical surface-based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface-based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface-based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one-way chi-squared test to test the null hypothesis that the population proportion was 50% (p = 0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false-positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre-evaluation, especially in subtle or visually unidentifiable FCD lesions. This time-efficient method would also help doctors identify otherwise overlooked details.

Aerobic exercise training decreases cognitive impairment caused by demyelination by regulating ROCK signaling pathway in aging mice

Recent studies have discovered a strong link between physical exercise and the prevention of neuro-degenerative symptoms, especially in elderly subjects, nonetheless, the exact underlying mechanism remains unclear. In this study, we hypothesized that aerobic exercise training may have a protective effect on myelin sheath in aged mice by regulating the ROCK signal pathway, which is considered as a crucial mechanism for decreasing apoptosis and promoting regeneration. Briefly, C57/BL aged mice underwent an exercise training (5 days/week, lasting 6 weeks). Memory and cognitive impairment were examined using Novel object recognition (NOR) test and Morris water maze test (MWM). Demyelination was explored using Luxol fast blue staining and transmission electron microscopy in the corpus callosum (CC), and the expression of ROCK and apoptotic protein were analyzed via western blot. We demonstrated the impairment of memory and cognitive and the decrease of myelin sheath thickness in aged mice. In addition, severe demyelination was observed in aged mice, accompanied with increased expression of RhoA, ROCK, ATF3, and Caspase 3, and reduced expression of MBP, Olig2, and NG2. Aerobic exercise training improved behavioral functions, increased the expression of MBP and myelin sheath thickness, reduced apoptosis and promoted myelination. To sum up, our data indicate that aerobic exercise training protects demyelination from aging-related white matter injury, which is associated with the up-regulation of ROCK signal pathway.

Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

Background and Objectives

Focal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.

Methods

Magnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.

Results

Neural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.

Conclusion

Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.