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Development and validation of a brain maturation index using longitudinal neuroanatomical scans
... A promising method in adult and neonatal neuroscience is the study of the "brain maturation index" (also known as "brain delta" or "predicted age difference") corresponding to the apparent age of the subject as compared to the norm (Dosenbach et al. 2010;Cao et al. 2015;Jonsson et al. 2019;Liem et al. 2017;Smith et al. 2019). By training regression models to fit the age of subjects from large normative imaging datasets, we can predict the age of individual subjects and compute the difference between the prediction and subject's true age. ...
... Brain maturation indices have been suggested as a powerful tool to capture alterations in the maturational trajectories of brain connectivity (Cao et al., 2015). Association with neurodevelopmental outcomes such as BSID-III is then important to evaluate the lasting impact of this predicted delay. ...
... In a similar way, we suggest that for an individual subject, a high deviation from the population norm, translating to age predictions significantly lower than true age (negative brain maturation index) can be a marker of potential developmental delay so that these subjects should undergo further tests and may need follow up. Therefore, this marker may provide an opportunity for early preventive intervention, as other similar studies suggest (Cao et al., 2015). ...
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment.
In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p<<0.001). We also achieved good accuracy when predicting gestational age at birth on a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<<0.001). From our models of PMA at scan for infants born at term, we computed the brain maturation index (i.e. predicted minus actual age) of individual preterm neonates and found significant correlation of this index with motor outcome at 18 months corrected age. Our results suggest that the neural substrate for later neurological functioning is detectable within a few weeks after birth in the structural connectome.
... Since structural brain changes in childhood and adolescence are expected to differ qualitatively and quantitatively from those in adulthood, a brain age model tailored to the younger age range would likely be more accurate and more sensitive to subtle aging effects in adolescents at risk. Several studies have been carried out in children and adolescents thus far, showing a mean absolute prediction error of around 1.0-1.7 years (Brown et al. 2012;Cao et al. 2015;Erus et al. 2015;Franke et al. 2012;Khundrakpam et al. 2015;Truelove-Hill et al. 2020). These deviations from the chronological age can be interpreted as delayed or accelerated brain development, making brain age a potential biomarker to detect deviant development. ...
... In that study, the EEG-based brain age gap was relatively stable over a period from childhood to adolescence. The MRI-based maturation index, a measure related to brain age, has also been found to be relatively stable throughout development (Cao et al. 2015). It remains an open question whether the temporal dynamics of brain age gaps over time are genetically driven. ...
Children and adolescents show high variability in brain development. Brain age-the estimated biological age of an individual brain-can be used to index developmental stage. In a longitudinal sample of adolescents (age 9-23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental ("aging") patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.
... 53 ) and stability was also proved when considering only developmental subjects (7-20 years). Several age prediction models ranging from early childhood to young adulthood have been developed so far ( Ball et al., 2017;Brown et al., 2012;Cao et al., 2015;Dosenbach et al., 2010;Erus et al., 2015;Khundrakpam et al., 2015;Wang et al., 2014 ), with accuracies for brain age predictions ranging in from = 0 . 43 to = 0 . ...
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7−64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson’s correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.
... Indeed, several reviewed studies did not include any description of QC. Others tended to focus on the quality of either raw or processed images, but rarely both (e.g., Cao et al., 2015). Limited detail was also provided about the criteria employed and manual corrections undertaken. ...
Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.
... To further confirm our result and to perform empirical evaluation of the predictive power of the SCN-based brain age estimator, we used an additional unseen testing dataset for evaluating the generalizability of the proposed analytical framework. Commensurate predictive accuracy in the independent testing dataset affirmed the generalizability of the constructed SCN-based brain age estimator, which was comparable with the structure-based prediction models in previous studies that evaluated brain age with high accuracy, on the basis of different brain anatomical features such as GMV (Franke et al. 2010;Cao et al. 2015;Cole et al. 2017), WM integrity (Mwangi et al. 2013;Lin et al. 2016), cortical thickness (Khundrakpam et al. 2015;Aycheh et al. 2018), and multimodal features (Brown et al. 2012;Erus et al. 2015;Liem et al. 2017). However, unlike previous studies, our study could fill the gap by including a larger sample of participants in middle-to-late adulthood and further extending the knowledge on network-level information for brain age prediction in this critical period of human aging process. ...
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
... A number of other studies establishing models for brain maturation including age ranges from early childhood to young adulthood have been published so far (58)(59)(60)(61)(62)(63). Accuracies for brain age predictions derived from cross-validation in the reference sample ranged from r = 0.43-0.96 ...
With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The “Brain Age Gap Estimation (BrainAGE)” method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.
... We employed k-fold cross-validation to determine the optimal lambda which was the one that resulted in the lowest cross-validated mean squared error (MSE). This method is beginning to gain traction in neuroimaging research, and has been used in functional imaging studies for voxel selection and in structural imaging studies to construct a "brain maturation index" of lasso-selected ROIs (Cao et al., 2015;Chang et al., 2015;Cribben et al., 2012). ...
In the United States over one-third of the population, including children and adolescents, are overweight or obese. Despite the prevalence of obesity, few studies have examined how food cravings and the ability to regulate them change throughout development. Here, we addressed this gap in knowledge by examining structural brain and behavioral changes associated with regulation of craving across development. In a longitudinal design, individuals ages 6–26 completed two structural scans as well as a behavioral task where they used a cognitive regulatory strategy to decrease the appetitive value of foods. Behaviorally, we found that the ability to regulate craving improved with age. Neurally, improvements in regulatory ability were associated with cortical thinning in medial and lateral prefrontal cortex. We also found that models with cortical thickness measurements and age chosen by a lasso-based variable selection method could predict an individual’s regulation behavior better than age and other behavioral factors alone. Additionally, when controlling for age, smaller ventral striatal volumes were associated with higher body mass index and predicted greater increases in weight two years later. Taken together, these results demonstrate a role for structural brain changes in supporting the ability to resist cravings for appetitive foods across development. Keywords: Longitudinal, Neuroimaging, Food, Emotion regulation, Child development, Adolescent development, Reward, Brain structure
... Prior studies using this method have yielded "brain age" estimates based on magnetic resonance imaging (MRI) scans that closely track with the true chronological ages of typically developing individuals. 21,22 Such a metric has been shown to be highly replicable, [23][24][25][26][27][28][29] heritable, 23 and robust to confounders, such as scanner-related noise 24,26 and head motion. 24 Prior studies of adult samples of patients with schizophrenia have applied this framework to show a pattern of accelerated brain aging in the patient groups compared with controls. ...
Importance
Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear.
Objective
To investigate distinct neuroanatomical markers that can differentiate aberrant neurodevelopmental trajectories among clinically high-risk (CHR) individuals.
Design, Setting, and Participants
In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1-weighted magnetic resonance imaging scans of 953 healthy controls 3 to 21 years of age from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and then applied to scans of 275 CHR individuals (including 39 who developed psychosis) and 109 healthy controls 12 to 21 years of age from the North American Prodrome Longitudinal Study 2 (NAPLS 2) for external validation and clinical application. Scans from NAPLS 2 were collected from January 15, 2010, to April 30, 2012.
Main Outcomes and Measures
Discrepancy between neuroanatomical-based predicted age (hereafter referred to as brain age) and chronological age.
Results
The PING-derived model (460 females and 493 males; age range, 3-21 years) accurately estimated the chronological ages of the 109 healthy controls in the NAPLS 2 (43 females and 66 males; age range, 12-21 years), providing evidence of independent external validation. The 275 CHR individuals in the NAPLS 2 (111 females and 164 males; age range, 12-21 years) showed a significantly greater mean (SD) gap between model-predicted age and chronological age (0.64 [2.16] years) compared with healthy controls (P = .008). This outcome was significantly moderated by chronological age, with brain age systematically overestimating the ages of CHR individuals who developed psychosis at ages 12 to 17 years but not the brain ages of those aged 18 to 21 years. Greater brain age deviation was associated with a higher risk for developing psychosis (F = 3.70; P = .01) and a pattern of stably poor functioning over time, but only among younger CHR adolescents. Previously reported evidence of accelerated reduction in cortical thickness among CHR individuals who developed psychosis was found to apply only to those who were 18 years of age or older.
Conclusions and Relevance
These results are consistent with the view that neuroanatomical markers of schizophrenia may help to explain some of the heterogeneity of this disorder, particularly with respect to early vs later age of onset of psychosis, with younger and older individuals having differing intercepts and trajectories in structural brain parameters as a function of age. The results also suggest that baseline neuroanatomical measures are likely to be useful in estimating onset of psychosis, especially (or only) among CHR individuals with an earlier age of onset of prodromal symptoms.
... First, our neural model of age is uni-modal and could be improved upon. For example, there are many possible brain measures that can be used as a proxy for maturity/age (Brown et al., 2012;Cao et al., 2015;Dosenbach et al., 2010;Khundrakpam, Tohka, Evans, and Group, 2015;Mwangi et al., 2013). We recommend future work integrate multi-modal brain measures (structure, function, connectivity, diffusion etc.) to produce a more comprehensive "brain maturity index". ...
Age is one of the best predictors of antisocial behavior. Risk models of recidivism often combine chronological age with demographic, social and psychological features to aid in judicial decision-making. Here we use independent component analyses (ICA) and machine learning techniques to demonstrate the utility of using brain-based measures of cerebral aging to predict recidivism. First, we developed a brain-age model that predicts chronological age based on structural MRI data from incarcerated males (n = 1332). We then test the model's ability to predict recidivism in a new sample of offenders with longitudinal outcome data (n = 93). Consistent with hypotheses, inclusion of brain-age measures of the inferior frontal cortex and anterior-medial temporal lobes (i.e., amygdala) improved prediction models when compared with models using chronological age; and models that combined psychological, behavioral, and neuroimaging measures provided the most robust prediction of recidivism. These results verify the utility of brain measures in predicting future behavior, and suggest that brain-based data may more precisely account for important variation when compared with traditional proxy measures such as chronological age. This work also identifies new brain systems that contribute to recidivism which has clinical implications for treatment development.
... To our knowledge, seven studies establishing models for brain development covering age ranges between early childhood and young adulthood have been published so far (Table 2; Brown et al., 2012;Cao et al., 2015;Dosenbach et al., 2010;Erus et al., 2015;Franke et al., 2012b;Khundrakpam et al., 2015;Wang et al., 2014). Accuracies for brain age predictions derived from cross-validation in the whole reference sample of healthy subjects ranged from r = 0.43-0.96 ...
ARTICLE INFO ABSTRACT Brain aging is a major determinant of aging. Along with the aging population, prevalence of neurodegenera-tive diseases is increasing, therewith placing economic and social burden on individuals and society. Individual rates of brain aging are shaped by genetics, epigenetics, and prenatal environmental. Biomarkers of biological brain aging are needed to predict individual trajectories of aging and the risk for age-associated neurological impairments for developing early preventive and interventional measures. We review current advances of in vivo biomarkers predicting individual brain age. Telomere length and epigenetic clock, two important biomarkers that are closely related to the mechanistic aging process, have only poor deterministic and predictive accuracy regarding individual brain aging due to their high intra-and interindividual variability. Phenotype-related bio-markers of global cognitive function and brain structure provide a much closer correlation to age at the individual level. During fetal and perinatal life, autonomic activity is a unique functional marker of brain development. The cognitive and structural biomarkers also boast high diagnostic specificity for determining individual risks for neurodegenerative diseases.
... Benchmarks will then aid in formulating underlying principles of brain development based on the timing and rates of growth/maturation as suggested, for example, by the findings of MacNeill et al. (2018) showing that A-not-B neural correlates parallel behavioral competency that then plateaus. While some work has begun to formulate indices of brain maturation and brain development phase (Brown et al., 2012;Cao et al., 2015), including evidence that brain-derived age correlates well with chronological age (Bunge & Whitaker, 2012), we currently do not have anything close to the weight and height charts used to measure physical development. We do not have population-based metrics, such as the T-scores we often use to characterize socioemotional behavior. ...
Multiple and rapid changes in brain development occur in infancy and early childhood that undergird behavioral development in core domains. The period of adolescence also carries a second influx of growth and change in the brain to support the unique developmental tasks of adolescence. This special section documents two core conclusions from multiple studies. First, evidence for change in brain‐based metrics that underlie cognitive and behavioral functions are not limited to narrow windows in development, but are evident from infancy into early adulthood. Second, the specific evident changes are unique to challenges and goals that are salient for a respective developmental period. These brain‐based changes interface with environmental inputs, whether from the child's broader ecology or at an individual level.
... Even though the cross-validation and SVR algorithm might help to prevent overfitting and provide generalization of methods and findings, further validation on larger and independent samples, preferably from multiple centers 41 , such as the Global ECT-MRI Research Collaboration (GEMRIC) 42 , will be necessary. The sample size of the current study also limited the possibility to fully explore the best features and algorithm with sufficient validations and to optimize for different populations (e.g., sex, stage) over lifespan [43][44][45][46][47][48] . Although the cross-validation procedure confirmed that the novel segmentation method of hippocampal subfields was reliable, further validation of the anatomical accuracy using in vitro brain tissues and manual tracing on a large sample might still be necessary. ...
Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a granule cell layer (GCL), a molecular layer (ML), and the subiculum. It is unclear which subfields are affected by ECT and whether we predict the future treatment response to ECT by using volumetric information of hippocampal subfields at baseline? In this study, 24 patients with severe MDD received the ECT and their structural brain images were acquired with magnetic resonance imaging before and after ECT. A state-of-the-art hippocampal segmentation algorithm from Freesurfer 6.0 was used. We found that ECT induced volume increases in CA subfields, GCL, ML and subiculum. We applied a machine learning algorithm to the hippocampal subfield volumes at baseline and were able to predict the change in depressive symptoms (r = 0.81; within remitters, r = 0.93). Receiver operating characteristic analysis also showed robust prediction of remission with an area under the curve of 0.90. Our findings provide evidence for particular hippocampal subfields having specific roles in the response to ECT. We also provide an analytic approach for generating predictions about clinical outcomes for ECT in MDD.
... Indeed, several reviewed studies did not include any description of QC. Others tended to focus on the quality of either raw or processed images, but rarely both (e.g., Cao et al., 2015). Limited detail was also provided about the criteria employed and manual corrections undertaken. ...
Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.
... Deeper characterization of the neural coding and processing of social-emotional information will also inform both typical neurobiological profiles of adolescent emotional development and dimensions of dysregulation that characterize adolescents with affect system difficulties such as depression and anxiety. For example, a recent study reported the development of a brain maturation index using structural MRI images (Cao et al., 2015). This index was derived from a regression-based algorithm 'trained' to integrate changes in brain anatomy across age to enhance prediction accuracy and differentiation of individual brain maturity. ...
... Deeper characterization of the neural coding and processing of social-emotional information will also inform both typical neurobiological profiles of adolescent emotional development and dimensions of dysregulation that characterize adolescents with affect system difficulties such as depression and anxiety. For example , a recent study reported the development of a brain maturation index using structural MRI images (Cao et al., 2015). This index was derived from a regression-based algorithm 'trained' to integrate changes in brain anatomy across age to enhance prediction accuracy and differentiation of individual brain maturity. ...
Adolescents are commonly portrayed as highly emotional, with their behaviors often hijacked by their emotions. Research on the neural substrates of adolescent affective behavior is beginning to paint a more nuanced picture of how neurodevelopmental changes in brain function influence affective behavior, and how these influences are modulated by external factors in the environment. Recent neurodevelopmental models suggest that the brain is designed to promote emotion regulation, learning, and affiliation across development, and that affective behavior reciprocally interacts with age-specific social demands and different social contexts. In this review, we discuss current findings on neurobiological mechanisms of adolescents’ affective behavior and highlight individual differences in and social-contextual influences on adolescents’ emotionality. Neurobiological mechanisms of affective processes related to anxiety and depression are also discussed as examples. As the field progresses, it will be critical to test new hypotheses generated from the foundational empirical and conceptual work and to focus on identifying more precisely how and when neural networks change in ways that promote or thwart adaptive affective behavior during adolescence.
... Using these patterns, the model then transforms, or aggregates, the high-dimensional image data of each individual into a predicted age, or brain age. Comparable techniques have successfully been applied to MRI scans leading to age predictions in adults (14) and across the lifespan (15), as well as development/maturation indices in children and young adults (16)(17)(18)(19). The advantage of such techniques over univariate analyses is that they detect and use the coherence between voxels involved in aging and are capable of dealing with the large variation in brain structures between subjects. ...
Objective:
Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question.
Method:
In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample.
Results:
In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant.
Conclusions:
The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment.
... This model can provide predictions for any value of the clinical covariates, whether observed or not, and by evaluating the entire range of all covariates, we can derive disease spectra that describe the full range of normal variation (17). Our focus in this study is on charting variation across clinical predictor variables, but related multivariate regression approaches have been used to predict subject age with respect to a normal developmental trajectory (18,(25)(26)(27)(28). ...
Background:
Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories. However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts.
Methods:
We introduce normative modeling for parsing heterogeneity in clinical cohorts, while allowing predictions at an individual subject level. This approach aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that address heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N = 491).
Results:
We identify participants who are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention-deficit/hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality.
Conclusions:
Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as-possibly idiosyncratic-deviation from normal functioning. It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ.
In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
Objective
Irritability is a common characteristic in ADHD. We examined whether dysfunction in neural connections supporting threat and reward processing was related to irritability in adolescents and young adults with ADHD.
Method
We used resting-state fMRI to assess connectivity of amygdala and nucleus accumbens seeds in those with ADHD ( n = 34) and an age- and gender-matched typically-developing comparison group ( n = 34).
Results
In those with ADHD, irritability was associated with atypical functional connectivity of both seed regions. Amygdala seeds showed greater connectivity with right inferior frontal gyrus and caudate/putamen, and less connectivity with precuneus. Nucleus accumbens seeds showed altered connectivity with middle temporal gyrus and precuneus.
Conclusion
The irritability-ADHD presentation is associated with atypical functional connectivity of reward and threat processing regions with cognitive control and emotion processing regions. These patterns provide novel evidence for irritability-associated neural underpinnings in adolescents and young adults with ADHD. The findings suggest cognitive and behavioral treatments that address response to reward, including omission of an expected reward and irritability, may be beneficial for ADHD.
Background:
Neuroimage analysis has made it possible to perform various anatomical analyses of the brain regions and helps detect different brain conditions/ disorders. Recently, neuroimaging-driven estimation of brain age is introduced as a robust biomarker for detecting different diseases and health conditions.
Objective:
To present a comprehensive review of brain age frameworks concerning: i) designing view: an overview of brain age frameworks based on image modality and methods used, and ii) clinical aspect: an overview of the application of brain age frameworks for detection of neurological disorders or health conditions.
Methods:
PubMed is explored to collect 136 articles from January 2010 to June 2021 using Brain Age Estimation and Brain Imaging, along with combinations of other radiological terms.
Results & conclusion:
The studies presented in this review are evidence of using brain age estimation methods in detecting various brain diseases/conditions. The survey also highlights tools and methods for brain age estimation and addresses some future research directions.
The accuracy of brain age estimates from magnetic resonance (MR) images has improved with the advent of deep learning artificial intelligence (AI) models. However, most previous studies on predicting age emphasized aging from childhood to adulthood and old age, and few studies have focused on early brain development in children younger than 2 years of age. Here, we performed brain age estimates based on MR images in children younger than 2 years of age using deep learning. Our AI model, developed with one slice each of raw T1- and T2-weighted images from each subject, estimated brain age with a mean absolute error of 8.2 weeks (1.9 months). The estimates of our AI model were close to those of human specialists. The AI model also estimated the brain age of subjects with a myelination delay as significantly younger than the chronological age. These results indicate that the prediction accuracy of our AI model approached that of human specialists and that our simple method requiring less data and preprocessing facilitates a radiological assessment of brain development, such as monitoring maturational changes in myelination.
Background:
Offspring of individuals with bipolar disorder (BD) are at greater risk for developing BD. Adiponectin (ADP), a hormone produced by adipocytes, plays a central role in energy homeostasis, insulin sensitivity and inflammatory response. ADP is negatively correlated with Body Mass Index (BMI) and is abnormal in patients with BD. Understanding the role of ADP among these offspring may help identify those likely to develop BD. The primary objective of this paper was to compare ADP levels among offspring of individuals with BD (symptomatic [SO], and asymptomatic [AO]) to offspring of healthy parents (HC). The role of ADP on cognition and ROI-based gray matter values in SO and AO offspring was secondarily assessed and compared to HC.
Methods:
A cross-sectional study was conducted in China by the Guangzhou Brain Hospital in offspring of individuals with and without BD. Participants underwent neuropsychiatric and cognitive assessments, MRI scans and blood analyses. BMI z-scores (zBMI) were calculated adjusting for age and gender.
Results:
Analyses included 117 participants (HC = 48, AO = 36, SO = 33). No significant differences were observed in plasma levels of ADP optical density (OD) among HC, AO and SO participants. No significant interaction effects on cognition were observed between symptomatic status and ADP OD, symptomatic status and BMI z-score, nor symptomatic status, zBMI and ADP OD. Multivariate tests revealed a significant interaction between offspring symptomatic status, ADP OD, and zBMI on gray matter volume in the right cerebellum (p = 0.05).
Conclusion:
These findings suggest that an interaction exists between BMI and CNS structure.
Glia. 2017 Sep; 65 (9),1504–1520. DOI:10.1002/glia.23176. The above referenced article was published with an incorrect image and legend for Figure. The authors apologize for this error and provide the correct Figure and legend below: (Figure presented.) Gene expression changes in microglia following an immune challenge are related to development. Top 1,000 genes were selected between different group comparisons to input into DAVID gene functional annotation software (https://david.ncifcrf.gov/tools.jsp). Top seven highly enriched gene functional groups were chosen for representation of group differences: (a) P60 vs. E18, (b) P60 females vs. males, (c) P60 male LPS vs. SAL, (d) P60 female LPS vs. SAL. Immune response genes are represented as green bars, membrane protein and signaling molecules as purple bars, and miscellaneous genes as orange bars. (e) Heat map of gene expression changes depicts upregulation or downregulation of individual genes in different group comparisons. Red = upregulation, blue = downregulation [Color figure can be viewed at wileyonlinelibrary.com].
Objective:
To compare brain-derived neurotrophic factor (BDNF) levels between offspring of individuals with bipolar disorders (BD) and healthy controls (HCs) and investigate the effects of BDNF levels and body mass index (BMI) on brain structures.
Method:
Sixty-seven bipolar offspring and 45 HCs were included (ages 8-28). Structural images were acquired using 3.0 Tesla magnetic resonance imaging. Serum BDNF levels were measured using enzyme-linked immunosorbent assay. Multivariate and univariate analyses of covariance were conducted.
Results:
Significantly higher BDNF levels were observed among bipolar offspring, relative to HCs (P > 0.025). Offspring status moderated the association between BDNF and BMI (F1 =4.636, P = 0.034). After adjustment for relevant covariates, there was a trend for a significant interaction of group and BDNF on neuroimaging parameters (Wilks'λ F56,94 =1.463, P = 0.052), with significant effects on cerebellar white matter and superior and middle frontal regions. Brain volume and BDNF were positively correlated among HCs and negatively correlated among bipolar offspring. Interactions between BDNF and BMI on brain volumes were non-significant among HCs (Wilks'λ F28,2 =2.229, P = 0.357), but significant among bipolar offspring (Wilks'λ F28,12 =2.899, P = 0.028).
Conclusion:
Offspring status and BMI moderate the association between BDNF levels and brain structures among bipolar offspring, underscoring BDNF regulation and overweight/obesity as key moderators of BD pathogenesis.
Evidence suggests many neurological disorders emerge when normal neurodevelopmental trajectories are disrupted, i.e., when circuits or cells do not reach their fully mature state. Microglia play a critical role in normal neurodevelopment and are hypothesized to contribute to brain disease. We used whole transcriptome profiling with Next Generation sequencing of purified developing microglia to identify a microglial developmental gene expression program involving thousands of genes whose expression levels change monotonically (up or down) across development. Importantly, the gene expression program was delayed in males relative to females and exposure of adult male mice to LPS, a potent immune activator, accelerated microglial development in males. Next, a microglial developmental index (MDI) generated from gene expression patterns obtained from purified mouse microglia, was applied to human brain transcriptome datasets to test the hypothesis that variability in microglial development is associated with human diseases such as Alzheimer's and autism where microglia have been suggested to play a role. MDI was significantly increased in both Alzheimer's Disease and in autism, suggesting that accelerated microglial development may contribute to neuropathology. In conclusion, we identified a microglia-specific gene expression program in mice that was used to create a microglia developmental index, which was applied to human datasets containing heterogeneous cell types to reveal differences between healthy and diseased brain samples, and between males and females. This powerful tool has wide ranging applicability to examine microglial development within the context of disease and in response to other variables such as stress and pharmacological treatments.
Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.
Recent studies of the structural and functional development of the human brain over the early years have highlighted the rapid development of brain structures and their interconnectivity. Some regional functional specializations emerge within the first months after birth, while others have a more protracted course of development spanning over the first decade or longer. While some anatomical changes enable the emergence of new functions, evidence also points to the importance of resting state oscillations in sculpting neural architecture during development. In atypical development differences in brain structure, function and task-related activity in infancy often precede the emergence of later diagnostic behavioural symptoms.
The advent of magnetic resonance imaging, which safely allows in vivo quantification of anatomical and physiologic features of the brain, has revolutionized pediatric neuroscience. Longitudinal studies are useful for the characterization of developmental trajectories (i.e. changes in imaging measures by age). Developmental trajectories (as opposed to static measures) have proven to have greater power in discriminating healthy from clinical groups and in predicting cognitive/behavioral measures such as IQ. Here we summarize results from an ongoing longitudinal pediatric neuroimaging study that has been conducted at the Child Psychiatry Branch of the National Institute of Mental Health since 1989. Developmental trajectories of structural MRI brain measures from healthy youth are compared and contrasted to trajectories in Attention-Deficit/Hyperactivity Disorder (ADHD) and Childhood-onset Schizophrenia. Across ages 5 to 25 years, in both healthy and clinical populations, white matter volumes increase and gray matter volumes follow an inverted U trajectory with peak size occurring at different times in different regions. At a group level, differences related to psychopathology are seen for gray and white matter volumes, rates of change, and for interconnectedness amongst disparate brain regions.Neuropsychopharmacology Reviews accepted article preview online, 08 September 2014. doi:10.1038/npp.2014.236.
Importance
Psychosis-risk studies have examined help-seeking adolescents and young adults. Population-based studies evaluating psychotic symptoms and neurocognitive performance across childhood are needed for “growth charting” cognitive development. We hypothesized that psychosis spectrum youths have delayed neurocognitive age relative to chronological age. We expected larger lags with increased symptom severity and in late adolescence and early adulthood.Objectives
To examine neurocognitive age and compare typically developing participants with psychosis spectrum participants.Design, Setting, and Participants
The Philadelphia Neurodevelopmental Cohort is a genotyped sample, with electronic medical records, enrolled in the study of brain behavior. In an academic and children’s hospital health care network, a structured psychiatric evaluation was performed and a computerized neurocognitive battery administered to evaluate performance in several domains. From 18 344 youths in the recruitment pool who were aged 8 to 21 years, physically and cognitively capable of participating, and proficient in English, participants were randomly selected with stratification for age, sex, and ethnicity. A total of 9138 participants were enrolled in the study between November 1, 2009, and November 30, 2011, and 2321 endorsed psychotic symptoms: 1423 significant (psychosis spectrum) and 898 limited (psychosis limited). They had no comorbid medical conditions. They were compared with 981 participants endorsing significant other psychiatric symptoms and with 1963 typically developing children with no psychiatric or medical disorders.Main Outcomes and Measures
The computerized neurocognitive battery provides accuracy and speed measures on 12 tests and speed measures alone on 2, yielding 26 measures used in a regression analysis to predict chronological age. Prediction was performed on the entire set and separately for each domain (executive, episodic memory, complex cognition, social cognition, and sensorimotor speed).Results
Throughout childhood and adolescence, the psychosis spectrum group had lower predicted age compared with the typically developing group and the group with other psychiatric symptoms. The psychosis spectrum group had a greater developmental lag than the psychosis limited group. The lags were most pronounced for complex cognition and social cognition and were smallest for sensorimotor speed.Conclusions and Relevance
Individuals who endorse psychotic symptoms are neurocognitively delayed across the age range; this delay relates to symptom severity and is not prominent in other psychiatric disorders. Combined clinical and neurocognitive assessment can facilitate early detection and targeted intervention to delay or ameliorate disease progression.
Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
Background
The Global Burden of Disease Study 2010 (GBD 2010) identified mental and substance use disorders as the 5th leading contributor of burden in 2010, measured by disability adjusted life years (DALYs). This estimate was incomplete as it excluded burden resulting from the increased risk of suicide captured elsewhere in GBD 2010's mutually exclusive list of diseases and injuries. Here, we estimate suicide DALYs attributable to mental and substance use disorders.
Methods
Relative-risk estimates of suicide due to mental and substance use disorders and the global prevalence of each disorder were used to estimate population attributable fractions. These were adjusted for global differences in the proportion of suicide due to mental and substance use disorders compared to other causes then multiplied by suicide DALYs reported in GBD 2010 to estimate attributable DALYs (with 95% uncertainty).
Results
Mental and substance use disorders were responsible for 22.5 million (14.8–29.8 million) of the 36.2 million (26.5–44.3 million) DALYs allocated to suicide in 2010. Depression was responsible for the largest proportion of suicide DALYs (46.1% (28.0%–60.8%)) and anorexia nervosa the lowest (0.2% (0.02%–0.5%)). DALYs occurred throughout the lifespan, with the largest proportion found in Eastern Europe and Asia, and males aged 20–30 years. The inclusion of attributable suicide DALYs would have increased the overall burden of mental and substance use disorders (assigned to them in GBD 2010 as a direct cause) from 7.4% (6.2%–8.6%) to 8.3% (7.1%–9.6%) of global DALYs, and would have changed the global ranking from 5th to 3rd leading cause of burden.
Conclusions
Capturing the suicide burden attributable to mental and substance use disorders allows for more accurate estimates of burden. More consideration needs to be given to interventions targeted to populations with, or at risk for, mental and substance use disorders as an effective strategy for suicide prevention.
We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07-1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.
We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longi-tudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer's disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive func-tioning in the future.
Bipolar disorder (BD) is a chronic illness with high morbidity and mortality. Pediatric onset BD has a more severe course of illness with higher rates of relapse and psychosocial impairment. Discovering interventions early in the course of BD in youth is paramount to preventing full illness expression and improve functioning in these individuals throughout the lifespan. It is therefore important to understand the mechanisms involved in the development of BD in order to determine which youth are at most risk and provide biological targets for early intervention. To serve this cause, we propose a neurodevelopmental model of BD, based on the existing data that implicate prefrontal - subcortical network dysfunction, caused by pre-existing genetic susceptibility and triggered by pathological reactions to stress and chronic inflammatory processes.
Understanding human cortical maturation is a central goal for developmental neuroscience. Significant advances toward this goal have come from two recent strands of in vivo structural magnetic resonance imaging research: (1) longitudinal study designs have revealed that factors such as sex, cognitive ability, and disease are often better related to variations in the tempo of anatomical change than to variations in anatomy at any one time point; (2) largely cross-sectional applications of new surface-based morphometry (SBM) methods have shown how the traditional focus on cortical volume (CV) can obscure information about the two evolutionarily and genetically distinct determinants of CV: cortical thickness (CT) and surface area (SA). Here, by combining these two strategies for the first time and applying SBM in >1250 longitudinally acquired brain scans from 647 healthy individuals aged 3-30 years, we deconstruct cortical development to reveal that distinct trajectories of anatomical change are hidden within, and give rise to, a curvilinear pattern of CV maturation. Developmental changes in CV emerge through the sexually dimorphic and age-dependent interaction of changes in CT and SA. Moreover, SA change itself actually reflects complex interactions between brain size-related changes in exposed cortical convex hull area, and changes in the degree of cortical gyrification, which again vary by age and sex. Knowing of these developmental dissociations, and further specifying their timing and sex-biases, provides potent new research targets for basic and clinical neuroscience.
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include âÂÂ_1 (the lasso), âÂÂ_2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
Connectivity Map of the Brain
The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358 ) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation.
Early-onset schizophrenia appears to be clinically more severe than the adult-onset form of the disease. In a previous study, we showed that anatomically related grey and white matter abnormalities found in adolescents patients were larger and more widespread than what had been reported in the literature on adult schizophrenia. Particularly, we found novel structural abnormalities in the primary sensorimotor and premotor systems. Here, we tested alternative hypotheses: either this striking sensorimotor-related pattern is an artefact due to a better sensitivity of the methods, or apparent greater structural abnormalities in the early-onset population are specifically associated with earlier disease onset. Then, if we were to find such characteristic structural pattern, we would test whether these anatomical abnormalities would remain static or, conversely, show dynamic changes in the still developing brain. To address these questions, we combined a cross-sectional study of brain structure for adolescent-onset patients (n = 25) and adult-onset patients (n = 35) and respective matched healthy subjects with a longitudinal study of adolescent-onset patients (n = 12, representative subset of the cross-sectional group) and matched healthy controls for >2 years. Looking at differences between adolescent and adult patients' grey matter volume and white matter microstructure abnormalities, we first confirmed the specificity (especially in motor-related areas) and the greater severity of structural abnormalities in the adolescent patients. Closer examination revealed, however, that such greater anomalies seemed to arise because adolescent patients fail to follow the same developmental time course as the healthy control group. Longitudinal analysis of a representative subset of the adolescent patient and matched healthy populations corroborated the delayed and altered maturation in both grey and white matters. Structural abnormalities specific to adolescent-onset schizophrenia in the sensori-motor cortices and corticospinal tract were less marked or even disappeared within the longitudinal period of observation, grey matter abnormalities in adolescent patients evolving towards the adult-onset pattern as defined by recent meta-analyses of adult schizophrenia. Combining cross-sectional adolescent and adult datasets with longitudinal adolescent dataset allowed us to find a unique, abnormal trajectory of grey matter maturation regardless of the age at onset of symptoms and of disease duration, with a lower and later peak than for healthy subjects. Taken together, these results suggest common aetiological mechanisms for adolescent- and adult-onset schizophrenia with an altered neurodevelopmental time course in the schizophrenic patients that is particularly salient in adolescence.
Structural maturation of fiber tracts in the human brain, including an increase in the diameter and myelination of axons,
may play a role in cognitive development during childhood and adolescence. A computational analysis of structural magnetic
resonance images obtained in 111 children and adolescents revealed age-related increases in white matter density in fiber
tracts constituting putative corticospinal and frontotemporal pathways. The maturation of the corticospinal tract was bilateral,
whereas that of the frontotemporal pathway was found predominantly in the left (speech-dominant) hemisphere. These findings
provide evidence for a gradual maturation, during late childhood and adolescence, of fiber pathways presumably supporting
motor and speech functions.
Pediatric neuroimaging studies1, 2, 3, 4, 5, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.
Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
We report the dynamic anatomical sequence of human cortical gray matter development between the age of 4-21 years using quantitative four-dimensional maps and time-lapse sequences. Thirteen healthy children for whom anatomic brain MRI scans were obtained every 2 years, for 8-10 years, were studied. By using models of the cortical surface and sulcal landmarks and a statistical model for gray matter density, human cortical development could be visualized across the age range in a spatiotemporally detailed time-lapse sequence. The resulting time-lapse "movies" reveal that (i) higher-order association cortices mature only after lower-order somatosensory and visual cortices, the functions of which they integrate, are developed, and (ii) phylogenetically older brain areas mature earlier than newer ones. Direct comparison with normal cortical development may help understanding of some neurodevelopmental disorders such as childhood-onset schizophrenia or autism.
We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.
Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.
Copyright © 2015. Published by Elsevier Inc.
We present a brain development index (BDI) that concisely summarizes complex imaging patterns of structural brain maturation along a single dimension using a machine learning methodology. The brain was found to follow a remarkably consistent developmental trajectory in a sample of 621 subjects of ages 8-22 participating in the Philadelphia Neurodevelopmental Cohort, reflected by a cross-validated correlation coefficient between chronologic age and the BDI of r = 0.89. Critically, deviations from this trajectory related to cognitive performance. Specifically, subjects whose BDI was higher than their chronological age displayed significantly superior cognitive processing speed compared with subjects whose BDI was lower than their actual age. These results indicate that the multiparametric imaging patterns summarized by the BDI can accurately delineate trajectories of brain development and identify individuals with cognitive precocity or delay.
Correlational analysis is a cornerstone method of statistical analysis, yet most presentations of correlational techniques deal primarily with tests of significance. The focus of this article is obtaining explicit expressions for confidence intervals for functions of simple, partial, and multiple correlations. Not only do these permit tests of hypotheses about differences but they also allow a clear statement about the degree to which correlations differ. Several important differences of correlations for which tests and confidence intervals are not widely known are included among the procedures discussed. Among these is the comparison of 2 multiple correlations based on independent samples. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
Structural MRI allows unparalleled in vivo study of the anatomy of the developing human brain. For more than two decades [1], MRI research has revealed many new aspects of this multifaceted maturation process, significantly augmenting scientific knowledge gathered from postmortem studies. Postnatal brain development is notably protracted and involves considerable changes in cerebral cortical [2-4], subcortical [5], and cerebellar [6, 7] structures, as well as significant architectural changes in white matter fiber tracts [8-11] (see [12]). Although much work has described isolated features of neuroanatomical development, it remains a critical challenge to characterize the multidimensional nature of brain anatomy, capturing different phases of development among individuals. Capitalizing on key advances in multisite, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental brain phase can be assessed with much greater precision than has been possible using other biological measures, accounting for more than 92% of the variance in age. Further, our composite metric of morphology, diffusivity, and signal intensity shows that the average difference in phase among children of the same age is only about 1 year, revealing for the first time a latent phenotype in the human brain for which maturation timing is tightly controlled. VIDEO ABSTRACT:
Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside.
In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
Delineation of the cortical anomalies underpinning attention-deficit/hyperactivity disorder (ADHD) can powerfully inform pathophysiological models. We previously found that ADHD is characterized by a delayed maturation of prefrontal cortical thickness. We now ask if this extends to the maturation of cortical surface area and gyrification.
Two hundred thirty-four children with ADHD and 231 typically developing children participated in the study, with 837 neuroanatomic magnetic resonance images acquired longitudinally. We defined the developmental trajectories of cortical surfaces and gyrification and the sequence of cortical maturation, as indexed by the age at which each cortical vertex attained its peak surface area.
In both groups, the maturation of cortical surface area progressed in centripetal waves, both lateral (starting at the central sulcus and frontopolar regions, sweeping toward the mid and superior frontal gyrus) and medial (descending down the medial prefrontal cortex, toward the cingulate gyrus). However, the surface area developmental trajectory was delayed in ADHD. For the right prefrontal cortex, the median age by which 50% of cortical vertices attained peak area was 14.6 years (SE = .03) in ADHD, significantly later than in typically developing group at 12.7 years (SE = .03) [log-rank test χ(¹)² = 1300, p < .00001]. Similar, but less pronounced, delay was found in the left hemispheric lobes. There were no such diagnostic differences in the developmental trajectories of cortical gyrification.
The congruent delay in cortical thickness and surface area direct attention away from processes that selectively affect one cortical component toward mechanisms controlling the maturation of multiple cortical dimensions.
Magnetic resonance imaging (MRI) allows unprecedented access to the anatomy and physiology of the developing brain without the use of ionizing radiation. Over the past two decades, thousands of brain MRI scans from healthy youth and those with neuropsychiatric illness have been acquired and analyzed with respect to diagnosis, sex, genetics, and/or psychological variables such as IQ. Initial reports comparing size differences of various brain components averaged across large age spans have given rise to longitudinal studies examining trajectories of development over time and evaluations of neural circuitry as opposed to structures in isolation. Although MRI is still not of routine diagnostic utility for evaluation of pediatric neuropsychiatric disorders, patterns of typical versus atypical development have emerged that may elucidate pathologic mechanisms and suggest targets for intervention. In this review we summarize general contributions of structural MRI to our understanding of neurodevelopment in health and illness.
The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T(1)-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19-86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T(1)-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.
To date there is little information about brain development during infancy and childhood, although several quantitative studies have shown volume changes in adult brains. We performed three-dimensional magnetic resonance imaging (3D-MRI) in 28 healthy children aged 1 month to 10 years. We examined the volumes of whole brain and frontal and temporal lobes with an advanced method for segmenting images into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) compartments. Growth spurts of whole brain and frontal and temporal lobes could be seen during the first 2 years after birth. During this period the frontal lobes grew more rapidly than the temporal lobes, the right–left asymmetry was more noticeable in the temporal lobes than in the frontal lobes and the increase in GM was larger than that in WM in the temporal lobes. Subsequently, WM volume increased at a higher rate than GM volume throughout childhood. Quantitative information on normal brain development may play a pivotal role in clarifying brain neurodevelopmental abnormalities.
Children with symptoms of schizophrenia-spectrum disorder (N = 20) were compared to controls (N = 20) matched for age and socioeconomic status. Structural brain abnormalities were assessed with magnetic resonance imaging and functional brain abnormalities with neuropsychological tests. Children with schizophrenia-spectrum disorder had smaller amygdala and temporal cortex volumes, along with reduced callosal areas and an unusual pattern of neuroanatomic asymmetries. No differences were noted in overall brain volume, ventricular volume, hippocampal volume, or frontal area. Schizophrenia-spectrum children were also characterized by deficits in all neuropsychological functions examined. Some types of verbal memory and frontal lobe skills were especially deficient. These results support the hypothesis that children with schizophrenia-spectrum disorder have significant brain abnormalities, similar in some ways to those seen in adult schizophrenics. In conjunction with recent primate studies, the current results draw attention to the role of the amygdala as one relevant factor in the pathogenesis of schizophrenia.
The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system.
Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.
To review recent neuroimaging studies of serious emotional disorders in youth and identify problems and promise of neuroimaging in clinical practice.
Published reports from refereed journals are briefly described, critiqued, and synthesized into a summary of the findings to date.
Childhood-onset schizophrenia shows progressive ventricular enlargement, reduction in total brain and thalamus volume, changes in temporal lobe structures, and reductions in frontal metabolism. Autistic disorder is associated with cerebellar changes, greater total brain and lateral ventricle volume, and asymmetry. The prefrontal cortex and the basal ganglia are consistently reported as abnormal in attention-deficit/hyperactivity disorder. Patients with anorexia nervosa show enlarged CSF spaces and reductions in gray and white matter that are only partially reversible with weight recovery.
Results from neuroimaging studies of childhood-onset psychiatric disorders suggest consistency in the structures found to be abnormal, but inconsistencies in the nature of these abnormalities. Although neuroimaging technology holds great promise for neurodevelopmental research, it is not yet a diagnostic instrument.
Despite significant gains in the fields of pediatric neuroimaging and developmental neurobiology, surprisingly little is known about the developing human brain or the neural bases of cognitive development. This paper addresses MRI studies of structural and functional changes in the developing human brain and their relation to changes in cognitive processes over the first few decades of human life. Based on post-mortem and pediatric neuroimaging studies published to date, the prefrontal cortex appears to be one of the last brain regions to mature. Given the prolonged physiological development and organization of the prefrontal cortex during childhood, tasks believed to involve this region are ideal for investigating the neural bases of cognitive development. A number of normative pediatric fMRI studies examining prefrontal cortical activity in children during memory and attention tasks are reported. These studies, while largely limited to the domain of prefrontal functioning and its development, lend support for continued development of attention and memory both behaviorally and physiologically throughout childhood and adolescence. Specifically, the magnitude of activity observed in these studies was greater and more diffuse in children relative to adults. These findings are consistent with the view that increasing cognitive capacity during childhood may coincide with a gradual loss rather than formation of new synapses and presumably a strengthening of remaining synaptic connections. It is clear that innovative methods like fMRI together with MRI-based morphometry and nonhuman primate studies will transform our current understanding of human brain development and its relation to behavioral development.
The purpose of the present study was to describe in greater anatomical detail the changes in brain structure that occur during maturation between childhood and adolescence. High-resolution MRI, tissue classification, and anatomical segmentation of cortical and subcortical regions were used in a sample of 35 normally developing children and adolescents between 7 and 16 years of age (mean age 11 years; 20 males, 15 females). Each cortical and subcortical measure was examined for age and sex effects on raw volumes and on the measures as proportions of total supratentorial cranial volume. Results indicate age-related increases in total supratentorial cranial volume and raw and proportional increases in total cerebral white matter. Gray-matter volume reductions were only observed once variance in total brain size was proportionally controlled. The change in total cerebral white-matter proportion was significantly greater than the change in total cerebral gray-matter proportion over this age range, suggesting that the relative gray-matter reduction is probably due to significant increases in white matter. Total raw cerebral CSF volume increases were also observed. Within the cerebrum, regional patterns varied depending on the tissue (or CSF) assessed. Only frontal and parietal cortices showed changes in gray matter, white matter, and CSF measures. Once the approximately 7% larger brain volume in males was controlled, only mesial temporal cortex, caudate, thalamus, and basomesial diencephalic structures showed sex effects with the females having greater relative volumes in these regions than the males. Overall, these results are consistent with earlier reports and describe in greater detail the regional pattern of age-related differences in gray and white matter in normally developing children and adolescents.
We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
Neuroanatomical structures may be profoundly or subtly affected by the interplay of genetic and environmental factors, age, and disease. Such effects are particularly true in healthy ageing individuals and in those who have neurodegenerative diseases. The ability to use imaging to identify structural brain changes associated with different neurodegenerative disease states would be useful for diagnosis and treatment. However, early in the progression of such diseases, neuroanatomical changes may be too mild, diffuse, or topologically complex to be detected by simple visual inspection or manually traced measurements of regions of interest. Computerised methods are being developed that can capture the extraordinary morphological variability of the human brain. These methods use mathematical models sensitive to subtle changes in the size, position, shape, and tissue characteristics of brain structures affected by neurodegenerative diseases. Neuroanatomical features can be compared within and between groups of individuals, taking into account age, sex, genetic background, and disease state, to assess the structural basis of normality and disease. In this review, we describe the strengths and limitations of algorithms of existing computer-assisted tools at the most advanced stage of development, together with available and foreseeable evidence of their usefulness at the clinical and research level.
We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools.
We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.