Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

A genome-wide CNV analysis of schizophrenia reveals a potential role for a multiple-hit model.

Schizophrenia is a chronic and severe psychiatric disorder that is highly heritable. While both common and rare genetic variants contribute to disease risk, many questions still remain about disease etiology. We performed a genome-wide analysis of copy number variants (CNVs) in 166 schizophrenia subjects and 52 psychiatrically healthy controls. First, overall CNV characteristics were compared between cases and controls. The only statistically significant finding was that deletions comprised a greater proportion of CNVs in cases. High interest CNVs were then identified as conservative using the following filtering criteria: (i) known deleterious CNVs; (ii) CNVs > 1 Mb that were novel (not found in a database of control individuals); and (iii) CNVs < 1 Mb that were novel and that overlapped the coding region of a gene of interest. Cases did not harbor a higher proportion of conservative CNVs in comparison to controls. However, similar to previous reports, cases had a slightly higher proportion of individuals with clinically significant CNVs (known deleterious or conservative CNVs > 1 Mb) or with multiple conservative CNVs. Two case individuals with the highest burden of conservative CNVs also share a recurrent 15q11.2 BP1-2 deletion, indicating a role for a potential multiple-hit CNV model for schizophrenia. In total, we report three 15q11.2 BP1-2 deletion individuals with schizophrenia, adding to a growing body of evidence that this CNV is involved in disease etiology.

Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia.

Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA+joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA+jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA+jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.

Eyeblink conditioning in healthy adults: a positron emission tomography study.

Eyeblink conditioning is a paradigm commonly used to investigate the neural mechanisms underlying motor learning. It involves the paired presentation of a tone-conditioning stimulus which precedes and co-terminates with an airpuff unconditioned stimulus. Following repeated paired presentations a conditioned eyeblink develops which precedes the airpuff. This type of learning has been intensively studied and the cerebellum is known to be essential in both humans and animals. The study presented here was designed to investigate the role of the cerebellum during eyeblink conditioning in humans using positron emission tomography (PET). The sample includes 20 subjects (10 male and 10 female) with an average age of 29.2 years. PET imaging was used to measure regional cerebral blood flow (rCBF) changes occurring during the first, second, and third blocks of conditioning. In addition, stimuli-specific rCBF to unpaired tones and airpuffs ("pseudoconditioning") was used as a baseline level that was subtracted from each block. Conditioning was performed using three, 15-trial blocks of classical eyeblink conditioning with the last five trials in each block imaged. As expected, subjects quickly acquired conditioned responses. A comparison between the conditioning tasks and the baseline task revealed that during learning there was activation of the cerebellum and recruitment of several higher cortical regions. Specifically, large peaks were noted in cerebellar lobules IV/V, the frontal lobes, and cingulate gyri.

Cigarette smoking and white matter microstructure in schizophrenia.

The majority of patients with schizophrenia smoke cigarettes. Both nicotine use and schizophrenia have been associated with alterations in brain white matter microstructure as measured by diffusion tensor imaging (DTI). The purpose of this study was to examine fractional anisotropy (FA) in smoking and non-smoking patients with schizophrenia and in healthy volunteers. A total of 43 patients (28 smoking and 15 non-smoking) with schizophrenia and 40 healthy, non-smoking participants underwent DTI. Mean FA was calculated in four global regions of interest (ROIs) (whole brain, cerebellum, brainstem, and total cortical) as well as in four regional ROIs (frontal, temporal, parietal and occipital lobes). The non-smoking patient group had a significantly higher intellectual quotient (IQ) compared with the patients who smoked, and our results varied according to whether IQ was included as a covariate. Without IQ correction, significant between-group effects for FA were found in four ROIs: total brain, total cortical, frontal lobe and the occipital lobe. In all cases the FA was lower among the smoking patient group, and highest in the control group. Smoking patients differed significantly from non-smoking patients in the frontal lobe ROI. However, these differences were no longer significant after IQ correction. FA differences between non-smoking patients and controls were not significant. Among smoking and non-smoking patients with schizophrenia but not healthy controls, FA was correlated with IQ. In conclusion, group effects of smoking on FA in schizophrenia might be mediated by IQ. Further, low FA in specific brain areas may be a neural marker for complex pathophysiology and risk for diverse problems such as schizophrenia, low IQ, and nicotine addiction.

Fully automated analysis using BRAINS: AutoWorkup.

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.

MTHFR 677C --> T genotype disrupts prefrontal function in schizophrenia through an interaction with COMT 158Val --> Met.

Understanding how risk genes cumulatively impair brain function in schizophrenia could provide critical insights into its pathophysiology. Working memory impairment in schizophrenia has been associated with abnormal dopamine signaling in the prefrontal cortex, which is likely under complex genetic control. The catechol-O-methyltransferase (COMT) 158Val --> Met polymorphism (rs4680), which affects the availability of prefrontal dopamine signaling, consistently stratifies prefrontal activation during working memory performance. However, the low-dopamine COMT 158Val allele does not confer increased risk for schizophrenia, and its effects on prefrontal function are not specific to the disorder. In the setting of other genetic variants influencing prefrontal dopamine signaling, COMT 158Val --> Met genotype may exert disease-specific effects. A second polymorphism, methylenetetrahydrofolate reductase (MTHFR) 677C --> T (rs1801133), has been associated with overall schizophrenia risk and executive function impairment in patients, and may influence dopamine signaling through mechanisms upstream of COMT effects. We found that the hypofunctional 677T variant was associated with decreased working memory load-dependent activation in the prefrontal and insular cortices in 79 schizophrenia patients, but not in 75 demographically matched healthy controls. Further, significant MTHFR x COMT genotype interactions were observed, which differed by diagnostic group: Reduced prefrontal activation was associated with the 677T and 158Val alleles in patients, but with 677C/C and 158Met/Met genotype in controls. These findings are consistent with epistatic effects of the COMT and MTHFR polymorphisms on prefrontal dopamine signaling, and suggest that in schizophrenia patients, the MTHFR 677T allele exacerbates prefrontal dopamine deficiency. The findings also suggest the importance of weighing COMT effects on prefrontal function within the context of MTHFR genotype.

What is post-traumatic stress disorder?

Although post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) are categorized as separate and discrete disorders, the boundary between them is sometimes indistinct. Their separation is based on the assumption that PTSD results primarily from psychological stress, while TBI is the consequence of an identifiable injury to the brain. This distinction is based on an antiquated polarity between mind and brain, and the separation of the two disorders often becomes arbitrary in day-to-day psychiatric practice and research.

WITHDRAWN: Erratum to "Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia" [NeuroImage 49 (2010) 2626-2637].

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Does function follow form?: methods to fuse structural and functional brain images show decreased linkage in schizophrenia.

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI-fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI-fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI-fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.

The lifetime trajectory of schizophrenia and the concept of neurodevelopment.

Defining the lifetime trajectory of schizophrenia and the mechanisms that drive it is one of the major challenges of schizophrenia research. Kraepelin assumed that the mechanisms were neurodegenerative ("dementia praecox"), and the early imaging work using computerized tomography seemed to support this model. Prominent ventricular enlargement and increased cerebrospinal fluid on the brain surface suggested that the brain had atrophied. In the 1980s, however, both neuropathological findings and evidence from magnetic resonance imaging (MRI) provided evidence suggesting that neurodevelopmental mechanisms might be a better explanation. This model is supported by both clinical and MRI evidence, particularly the fact that brain abnormalities are already present in first-episode patients. However, longitudinal studies of these patients have found evidence that brain tissue is also lost during the years after onset. The most parsimonious explanation of these findings is that neurodevelopment is a process that is ongoing throughout life, and that schizophrenia occurs as a consequence of aberrations in neurodevelopmental processes that could occur at various stages of life.

G72 influences longitudinal change in frontal lobe volume in schizophrenia.

Schizophrenia is a neurodevelopmental psychiatric disorder characterized by a variety of structural brain abnormalities that appear to progress across the course of illness. Schizophrenia also is highly heritable, and one gene that has emerged as a possible susceptibility factor is G72. G72 influences brain development and activity by an as-yet unclear mechanism, and multiple studies have reported associations between G72 and schizophrenia. We were interested in linking these domains of investigation by determining whether G72 also influences the rate of longitudinal structural brain changes in individuals with schizophrenia. As part of the Iowa Longitudinal Study of Recent Onset Psychoses, we genotyped four G72 polymorphisms previously associated with schizophrenia in 110 subjects with schizophrenia or schizoaffective disorder from whom we had obtained two brain MRI scans an average of 3 years apart. The four polymorphisms captured three haplotypes, one of which was strongly associated with an increased rate of frontal lobe volume decrement. This same haplotype was also associated with more severe psychotic symptoms at the time of the second scan. These data thus suggest that variation in G72 modulates the progressive brain changes that characterize schizophrenia.

Patients on the psychosis spectrum employ an alternate brain network to engage in complex decision-making.

Brain reward processing mechanisms that underlie complex decision-making are compromised in psychosis. The goal of this research was to advance our understanding of the underlying (1) neural mechanisms and (2) discrete neuro-economic/motivational processes that may be altered in complex decision-making in euthymic patients on the psychosis spectrum (PPS). Utilizing a functional magnetic resonance neuroimaging (fmri) paradigm of a well-validated laboratory measure of complex decision-making (Iowa Gambling Task-IGT), the brain activation patterns of a target group of PPS were compared to a demographically matched healthy comparison group (HMC). These two groups were also evaluated on real-life decision outcomes on day of scan. PPS primarily activate the Dorsal Attentional Network (DAN) in real-life decision outcomes and in achieving similar levels of performance on the IGT as the HMC, in-spite of dysregulated dopamine-based brain-reward circuit and salience network fmri activation patterns. However, PPS report more significant negative outcomes of their decision-making in real-life, compared to HMC. The differential engagement of brain networks by PPS on the IGT appear to be moderated by antipsychotic, dopamine antagonist, medication lifetime/daily dose levels. These findings may also be mediated by extent of dysregulation in brain reward circuitry and salience network associated with psychosis severity in the target PPS group. This is also evident in case studies of unmedicated PPS. We conclude by suggesting that the brain may adapt to this dysregulation by co-opting the DAN network, which is implicated in the related function of problem-solving, towards complex decision-making. The extent of utilization of the DAN network in complex decision-making may be moderated by psychosis severity.

Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls.

Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low-frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task.

A method to fuse fMRI tasks through spatial correlations: applied to schizophrenia.

Single task analysis methods of functional MRI brain data, though useful, are not able to evaluate the joint information between tasks. Data fusion of multiple tasks that probe different cognitive processes provides knowledge of the joint information and may be important in order to better understand complex disorders such as schizophrenia. In this article, we introduce a simple but effective technique to fuse two tasks by computing the histogram of correlations for all possible combinations of whole brain voxels. The approach was applied to data derived from healthy controls and patients with schizophrenia from four different tasks, auditory oddball (target), auditory oddball (novel), Sternberg working memory, and sensorimotor. It was found that in four out of six task combinations patients' intertask correlations were more positively correlated than controls', in one combination the controls showed more positive correlations and in another there was no significant difference. The robustness of this result was checked with several testing techniques. The four task combinations for which patients had more positive correlation occurred at different scanning sessions and the task combination that showed the opposite result occurred within the same scanning session. Brain regions that showed high intertask correlations were found for both groups and regions that correlated differently between the two groups were identified. The approach introduced finds interesting results and new differential features that cannot be achieved through traditional methods.

Three-dimensional morphometric analysis of brain shape in nonsyndromic orofacial clefting.

Previous studies report structural brain differences in individuals with nonsyndromic orofacial clefts (NSOFC) compared with healthy controls. These changes involve non-uniform shifts in tissue volume within the cerebral cortex and cerebellum, suggesting that the shape of the brain may be altered in cleft-affected individuals. To test this hypothesis, a landmark-based morphometric approach was utilized to quantify and compare brain shape in a sample of 31 adult males with cleft lip with or without cleft palate (CL/P), 14 adult males with cleft palate only (CPO) and 41 matched healthy controls. Fifteen midline and surface landmarks were collected from MRI brain scans and the resulting 3D coordinates were subjected to statistical shape analysis. First, a geometric morphometric analysis was performed in three steps: Procrustes superimposition of raw landmark coordinates, omnibus testing for group difference in shape, followed by canonical variates analysis (CVA) of shape coordinates. Secondly, Euclidean distance matrix analysis (EDMA) was carried out on scaled inter-landmark distances to identify localized shape differences throughout the brain. The geometric morphometric analysis revealed significant differences in brain shape among all three groups (P < 0.001). From CVA, the major brain shape changes associated with clefting included selective enlargement of the anterior cerebrum coupled with a relative reduction in posterior and/or inferior cerebral portions, changes in the medio-lateral position of the cerebral poles, posterior displacement of the corpus callosum, and reorientation of the cerebellum. EDMA revealed largely similar brain shape changes. Thus, compared with controls, major brain shape differences were present in adult males with CL/P and CPO. These results both confirm and expand previous findings from traditional volumetric studies of the brain in clefting and provide further evidence that the neuroanatomical phenotype in individuals with NSOFC is a primary manifestation of the defect and not a secondarily acquired characteristic.

Sex differences in parietal lobe morphology: relationship to mental rotation performance.

Structural magnetic resonance imaging (MRI) studies of the human brain have reported evidence for sexual dimorphism. In addition to sex differences in overall cerebral volume, differences in the proportion of gray matter (GM) to white matter (WM) volume have been observed, particularly in the parietal lobe. To our knowledge there have been no studies examining the relationship between the sex differences in parietal lobe structure and function. The parietal lobe is thought to be involved in spatial ability, and particularly involved in mental rotation. The purpose of this study is to examine whether sex differences in parietal lobe structure are present, and if present to relate these differences to performance on the mental rotations test (MRT). We found that women had proportionately greater gray matter volume in the parietal lobe compared to men, and this morphologic difference was disadvantageous for women in terms of performance on the MRT. In contrast, we found that men compared to women had proportionately greater parietal lobe surface area, and this morphologic difference was associated with a performance advantage for men on mental rotation. These findings support the possibility that the sexual dimorphism in the structure of the parietal lobe is a neurobiological substrate for the sex difference in performance on the mental rotations test.

Morphology of the ventral frontal cortex: relationship to femininity and social cognition.

Females have been shown in a number of studies to be more adept in social perception compared with males. In addition, studies have reported that brain regions important in interpretation of nonverbal social cues, such as the ventral frontal cortex (VFC), are morphologically different between genders. To investigate the relationship between the structure of the VFC and social cognition, gray matter volume and surface area of the VFC were measured on magnetic resonance imaging (MRI) scans from 30 men and 30 women matched for age and IQ. The VFC was subdivided into the orbitofrontal cortex (OFC) and the straight gyrus (SG). The SG, but not the OFC, was proportionately larger in women. A subset of subjects was administered the Interpersonal Perception Task (IPT), a test of social perceptiveness, and the Personal Attributes Questionnaire (PAQ), a scale of femininity and masculinity. Identification with more feminine traits on the PAQ correlated with greater SG gray matter volume and surface area. In addition, higher degrees of femininity correlated with better performance on the IPT. Taken together, these data suggest a complex relationship between femininity, social cognition, and the structure of the SG.

Distributed neural efficiency: Intelligence and age modulate adaptive allocation of resources in the brain.

Whether superior intelligence is associated with global lower resource consumption in the brain remains unresolved. In order to tap fluid intelligence "Gf" cortical networks, 50 neurologically healthy adults were functionally neuro-imaged while solving a modified version of the Raven Advanced Progressive Matrices. "Gf" predicted increased activation of key components of the dorsal attention network (DAN); and age predicted extent of simultaneous deactivation in key components of the default mode network (DMN) during problem-solving. However, there was no significant difference in mean levels of whole brain activation even when cognitively taxed. This suggests that the brain may dynamically switch resource consumption between these anti-correlated DAN and DMN networks, concentrating processing power in regions critical to enhanced cognitive performance. We term this mechanism of activation increase and decrease of these anti-correlated DAN/DMN systems, modulated by "Gf" and age, as "distributed neural efficiency". This may achieve local cost-efficiency trade-offs, while maintaining global energy homeostasis.