In-scanner head motion and structural covariance networks.

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 µm isotropic 7T MP2RAGE MRI.

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 µm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research.

Motion and morphometry in clinical and nonclinical populations.

INTRODUCTION: The relationship between participant motion, demographic variables and MRI-derived morphometric estimates was investigated in autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia and healthy controls. Participant motion was estimated using resting state fMRI and used as a proxy measure for motion during T1w MRI acquired in the same session. Analyses were carried out in scans qualitatively assessed as free from motion-related artifact. METHODS: Whole brain T1-weighted MRI and resting state fMRI acquisitions from the ABIDE, ADHD-200 and COBRE databases were included in our analyses. Motion was estimated using coregistration of sequential resting state volumes. We investigated if motion is related to diagnosis, age and gender, and scanning site. We further determined if there is a relationship between participant motion and cortical thickness, contrast, and volumetric estimates. RESULTS: 2141 participants were included in our analyses. Participant motion was higher in all clinical groups compared with healthy controls. Younger (age<20years) and older (age>40years) people move more than individuals aged 20-40years. Increased motion is associated with reduced average cortical thickness (-0.014mm thickness per mm motion, p=0.0014) and cortical contrast (0.77% contrast reduction per mm motion, p=2.16×10(-9)) in scans that have been qualitatively assessed as free from motion artifact. Volumetric estimates were also associated with motion, however the relationships were generally weaker than cortical thickness and contrast and were dependent on the segmentation method used. CONCLUSIONS: Participant motion is increased in clinical groups and is systematically associated with morphometric estimates. These findings indicate that accounting for participant motion may be important for improving the statistical validity of morphometric studies.

The corpus callosum and recovery of working memory after epilepsy surgery.

OBJECTIVE: For patients with medically intractable focal epilepsy, the benefit of epilepsy surgery must be weighed against the risk of cognitive decline. Clinical factors such as age and presurgical cognitive level partially predict cognitive outcome; yet, little is known about the role of cross-hemispheric white matter pathways in supporting postsurgical recovery of cognitive function. The purpose of this study is to determine whether the presurgical corpus callosum (CC) midsagittal area is associated with pre- to postsurgical change following epilepsy surgery. METHODS: In this observational study, we retrospectively identified 24 adult patients from an epilepsy resection series who completed preoperative high-resolution T1 -weighted magnetic resonance imaging (MRI) scans, as well as pre- and postsurgical neuropsychological testing. The total area and seven subregional areas of the CC were measured on the midsagittal MRI slice using an automated method. Standardized indices of auditory-verbal working memory and delayed memory were used to probe cognitive change from pre- to postsurgery. CC total and subregional areas were regressed on memory-change scores, after controlling for overall brain volume, age, presurgical memory scores, and duration of epilepsy. RESULTS: Patients had significantly reduced CC area relative to healthy controls. We found a positive relationship between CC area and change in working memory, but not delayed memory; specifically, the larger the CC, the greater the postsurgical improvement in working memory (ß = 0.523; p = 0.009). Effects were strongest in posterior CC subregions. There was no relationship between CC area and presurgical memory scores. SIGNIFICANCE: Findings indicate that larger CC area, measured presurgically, is related to improvement in working memory abilities following epilepsy surgery. This suggests that transcallosal pathways may play an important, yet little understood, role in postsurgical recovery of cognitive functions.

Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 µm MP2RAGE MRI at 7 T.

OBJECTIVES: The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution. MATERIALS AND METHODS: Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm3) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures. RESULTS: At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex (d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter (d = -0.81, P = 0.039), thalamus (d = 1.43, P < 0.001), globus pallidus (d = 0.92, P = 0.020), caudate nucleus (d = -0.83, P = 0.039), and corpus callosum (d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes (d = 1.05, P = 0.009). CONCLUSIONS: Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.

Sample size estimates for well-powered cross-sectional cortical thickness studies.

INTRODUCTION: Cortical thickness mapping is a widely used method for the analysis of neuroanatomical differences between subject groups. We applied power analysis methods over a range of image processing parameters to derive a model that allows researchers to calculate the number of subjects required to ensure a well-powered cross-sectional cortical thickness study. METHODS: 0.9-mm isotropic T1 -weighted 3D MPRAGE MRI scans from 98 controls (53 females, age 29.1 ± 9.7 years) were processed using Freesurfer 5.0. Power analyses were carried out using vertex-wise variance estimates from the coregistered cortical thickness maps, systematically varying processing parameters. A genetic programming approach was used to derive a model describing the relationship between sample size and processing parameters. The model was validated on four Alzheimer's Disease Neuroimaging Initiative control datasets (mean 126.5 subjects/site, age 76.6 ± 5.0 years). RESULTS: Approximately 50 subjects per group are required to detect a 0.25-mm thickness difference; less than 10 subjects per group are required for differences of 1 mm (two-sided test, 10 mm smoothing, α = 0.05). Sample size estimates were heterogeneous over the cortical surface. The model yielded sample size predictions within 2-6% of that determined experimentally using independent data from four other datasets. Fitting parameters of the model to data from each site reduced the estimation error to less than 2%. CONCLUSIONS: The derived model provides a simple tool for researchers to calculate how many subjects should be included in a well-powered cortical thickness analysis.

Mood and Anxiety Disorders and Suicidality in Patients With Newly Diagnosed Focal Epilepsy: An Analysis of a Complex Comorbidity.

BACKGROUND AND OBJECTIVES: Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS: The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS: A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION: In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

Selecting appropriate voxel-based methods for neuroimaging studies.

We highlight a fundamental difference between voxel-based methods that interrogate signal intensity directly and those that interrogate morphometric features; we discuss how signal intensity changes might erroneously affect morphometric measures, and we provide some guidance for selection of appropriate methods to address particular hyphotheses. Our discussion is motivated by a recent application of voxel-based morphometry methods to T2-weighted images (T2-Voxel Based Morphometry; T2-VBM). In this context we discuss alternative approaches including Voxel-Based T2-Relaxometry (VBR) and Voxel Based Iterative Sensitivity analysis of T2-Weighted Images (VBIS-T2).

Structural Neuroimaging in Adults and Adolescents With Newly Diagnosed Focal Epilepsy: The Human Epilepsy Project.

BACKGROUND AND OBJECTIVES: Identification of an epileptogenic lesion on structural neuroimaging in individuals with focal epilepsy is important for management and treatment planning. The objective of this study was to determine the frequency of MRI-identified potentially epileptogenic structural abnormalities in a large multicenter study of adolescent and adult patients with newly diagnosed focal epilepsy. METHODS: Patients with a new diagnosis of focal epilepsy enrolled in the Human Epilepsy Project observational cohort study underwent 3-Tesla (3T) brain MRI using a standardized protocol. Imaging findings were classified as normal, abnormal, or incidental. Abnormal findings were classified as focal or diffuse, and as likely epilepsy-related or of unknown relationship to epilepsy. Fisher exact tests were performed to determine whether abnormal imaging or abnormality type was associated with clinical characteristics. RESULTS: 418 participants were enrolled. 218 participants (59.3%) had no abnormalities detected, 149 (35.6%) had abnormal imaging, and 21 (5.0%) had incidental findings. 78 participants (18.7%) had abnormalities that were considered epilepsy-related and 71 (17.0%) had abnormalities of unknown relationship to epilepsy. Older participants were more likely to have imaging abnormalities, while participants with focal and epilepsy-related imaging abnormalities were younger than those without these abnormalities. 131 participants (31.3%) had a family history of epilepsy. Epilepsy-related abnormalities were not associated with participant sex, family history of epilepsy, or seizure type. DISCUSSION: We found that one in five patients with newly diagnosed focal epilepsy has an MRI finding that is likely causative and may alter treatment options. An additional one in five patients has abnormalities of unknown significance. This information is important for patient counseling, prognostication, and management.

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video.

INTRODUCTION: In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. METHODS: 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. RESULTS: Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. CONCLUSIONS: We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies.

BACKGROUND AND PURPOSE: In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols. METHODS: Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach. RESULTS: A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%. CONCLUSIONS: The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.

Hippocampal volume assessment in temporal lobe epilepsy: How good is automated segmentation?

PURPOSE: Quantitative measurement of hippocampal volume using structural magnetic resonance imaging (MRI) is a valuable tool for detection and lateralization of mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE). We compare two automated hippocampal volume methodologies and manual hippocampal volumetry to determine which technique is most sensitive for the detection of hippocampal atrophy in mTLE. METHODS: We acquired a three-dimensional (3D) volumetric sequence in 10 patients with left-lateralized mTLE and 10 age-matched controls. Hippocampal volumes were measured manually, and using the software packages Freesurfer and FSL-FIRST. The sensitivities of the techniques were compared by determining the effect size for average volume reduction in patients with mTLE compared to controls. The volumes and spatial overlap of the automated and manual segmentations were also compared. RESULTS: Significant volume reduction in affected hippocampi in mTLE compared to controls was detected by manual hippocampal volume measurement (p < 0.01, effect size 33.2%), Freesurfer (p < 0.01, effect size 20.8%), and FSL-FIRST (p < 0.01, effect size 13.6%) after correction for brain volume. Freesurfer correlated reasonably (r = 0.74, p << 0.01) with this manual segmentation and FSL-FIRST relatively poorly (r = 0.47, p << 0.01). The spatial overlap between manual and automated segmentation was reduced in affected hippocampi, suggesting the accuracy of automated segmentation is reduced in pathologic brains. DISCUSSION: Expert manual hippocampal volumetry is more sensitive than both automated methods for the detection of hippocampal atrophy associated with mTLE. In our study Freesurfer was the most sensitive to hippocampal atrophy in mTLE and could be used if expert manual segmentation is not available.

Hippocampal volumetric integrity in mesial temporal lobe epilepsy: A fast novel method for analysis of structural MRI.

OBJECTIVE: We investigate whether a rapid and novel automated MRI processing technique for assessing hippocampal volumetric integrity (HVI) can be used to identify hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (mTLE) and determine its performance relative to hippocampal volumetry (HV) and visual inspection. METHODS: We applied the HVI technique to T1-weighted brain images from healthy control (n = 35), mTLE (n = 29), non-HS temporal lobe epilepsy (TLE, n = 44), and extratemporal focal epilepsy (EXTLE, n = 25) subjects imaged using a standardized epilepsy research imaging protocol and on non-standardized clinically acquired images from mTLE subjects (n = 40) to investigate if the technique is translatable to clinical practice. Performance of HVI, HV, and visual inspection was assessed using receiver operating characteristic (ROC) analysis. RESULTS: mTLE patients from both research and clinical groups had significantly reduced ipsilateral HVI relative to controls (effect size: -0.053, 5.62%, p =  0.002 using a standardized research imaging protocol). For lateralizing mTLE, HVI had a sensitivity of 88% compared with a HV sensitivity of 92% when using specificity equal to 70%. CONCLUSIONS: The novel HVI approach can effectively detect HS in clinical populations, with an average image processing time of less than a minute. The fast processing speed suggests this technique could have utility as a quantitative tool to assist with imaging-based diagnosis and lateralization of HS in a clinical setting.

NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

Whole brain neuronal abnormalities in focal epilepsy quantified with proton MR spectroscopy.

OBJECTIVE: To test the hypothesis that localization-related epilepsy is associated with widespread neuronal dysfunction beyond the ictal focus, reflected by a decrease in patients' global concentration of their proton MR spectroscopy (1H-MRS) observed marker, N-acetyl-aspartate (NAA). METHODS: Thirteen patients with localization-related epilepsy (7 men, 6 women) 40±13 (mean±standard-deviation)years old, 8.3±13.4years of disease duration; and 14 matched controls, were scanned at 3 T with MRI and whole-brain (WB) 1H MRS. Intracranial fractions of brain volume, gray and white matter (fBV, fGM, fWM) were segmented from the MRI, and global absolute NAA creatine (Cr) and choline (Cho) concentrations were estimated from their WB 1H MRS. These metrics were compared between patients and controls using an unequal variance t test. RESULTS: Patients' fBV, fGM and fWM: 0.81±0.07, 0.47±0.04, 0.31±0.04 were not different from controls' 0.79±0.05, 0.48±0.04, 0.32±0.02; nor were their Cr and Cho concentrations: 7.1±1.1 and 1.3±0.2 millimolar (mM) versus 7.7±0.7 and 1.4±0.1mM (p>0.05 all). Patients' global NAA concentration: 11.5±1.5 mM, however, was 12% lower than controls' 13.0±0.8mM (p=0.004). CONCLUSIONS: These findings indicate that neuronal dysfunction in localization-related epilepsy extends globally, beyond the ictal zone, but without atrophy or spectroscopic evidence of other pathology. This suggests a diffuse decline in the neurons' health, rather than their number, early in the disease course. WB 1H-MRS assessment, therefore, may be a useful tool for quantification of global neuronal dysfunction load in epilepsy.

Structural brain changes in medically refractory focal epilepsy resemble premature brain aging.

OBJECTIVE: We used whole brain T1-weighted MRI to estimate the age of individuals with medically refractory focal epilepsy, and compared with individuals with newly diagnosed focal epilepsy and healthy controls. The difference between neuroanatomical age and chronological age was compared between the three groups. METHODS: Neuroanatomical age was estimated using a machine learning-based method that was trained using structural MRI scans from a large independent healthy control sample (N=2001). The prediction model was then used to estimate age from MRI scans obtained from newly diagnosed focal epilepsy patients (N=42), medically refractory focal epilepsy patients (N=94) and healthy controls (N=74). RESULTS: Individuals with medically refractory epilepsy had a difference between predicted brain age and chronological age that was on average 4.5 years older than healthy controls (p=4.6×10-5). No significant differences were observed in newly diagnosed focal epilepsy. Earlier age of onset was associated with an increased brain age difference in the medically refractory group (p=0.034). SIGNIFICANCE: Medically refractory focal epilepsy is associated with structural brain changes that resemble premature brain aging.

Structural MRI markers of brain aging early after ischemic stroke.

OBJECTIVE: To examine associations between ischemic stroke, vascular risk factors, and MRI markers of brain aging. METHODS: Eighty-one patients (mean age 67.5 ± 13.1 years, 31 left-sided, 61 men) with confirmed first-ever (n = 66) or recurrent (n = 15) ischemic stroke underwent 3T MRI scanning within 6 weeks of symptom onset (mean 26 ± 9 days). Age-matched controls (n = 40) completed identical testing. Multivariate regression analyses examined associations between group membership and MRI markers of brain aging (cortical thickness, total brain volume, white matter hyperintensity [WMH] volume, hippocampal volume), normalized against intracranial volume, and the effects of vascular risk factors on these relationships. RESULTS: First-ever stroke was associated with smaller hippocampal volume (p = 0.025) and greater WMH volume (p = 0.004) relative to controls. Recurrent stroke was in turn associated with smaller hippocampal volume relative to both first-ever stroke (p = 0.017) and controls (p = 0.001). These associations remained significant after adjustment for age, sex, education, and, in stroke patients, infarct volume. Total brain volume was not significantly smaller in first-ever stroke patients than in controls (p = 0.056), but the association became significant after further adjustment for atrial fibrillation (p = 0.036). Cortical thickness and brain volumes did not differ as a function of stroke type, infarct volume, or etiology. CONCLUSIONS: Brain structure is likely to be compromised before ischemic stroke by vascular risk factors. Smaller hippocampal and total brain volumes and increased WMH load represent proxies for underlying vascular brain injury.

Periventricular white matter abnormalities and restricted repetitive behavior in autism spectrum disorder.

Malformations of cortical development are found at higher rates in autism spectrum disorder (ASD) than in healthy controls on postmortem neuropathological evaluation but are more variably observed on visual review of in-vivo MRI brain scans. This may be due to the visually elusive nature of many malformations on MRI. Here, we utilize a quantitative approach to determine whether a volumetric measure of heterotopic gray matter in the white matter is elevated in people with ASD, relative to typically developing controls (TDC). Data from a primary sample of 48 children/young adults with ASD and 48 age-, and gender-matched TDCs, selected from the Autism Brain Imaging Data Exchange (ABIDE) open-access database, were analyzed to compare groups on (1) blinded review of high-resolution T1-weighted research sequences; and (2) quantitative measurement of white matter hypointensity (WMH) volume calculated from the same T1-weighted scans. Groupwise WMH volume comparisons were repeated in an independent, multi-site sample (80 ASD/80 TDC), also selected from ABIDE. Visual review resulted in equivalent proportions of imaging abnormalities in the ASD and TDC group. However, quantitative analysis revealed elevated periventricular and deep subcortical WMH volumes in ASD. This finding was replicated in the independent, multi-site sample. Periventricular WMH volume was not associated with age but was associated with greater restricted repetitive behaviors on both parent-reported and clinician-rated assessment inventories. Thus, findings demonstrate that periventricular WMH volume is elevated in ASD and associated with a higher degree of repetitive behaviors and restricted interests. Although the etiology of focal WMH clusters is unknown, the absence of age effects suggests that they may reflect a static anomaly.