Within-network brain connectivity during a social optimism task is related to personal optimism and optimism for in-group members.

Optimism bias is the tendency to believe desirable events are more likely to happen than undesirable ones. People often display optimistic biases for themselves (personal optimism), but also for members of groups they like or identify with (social optimism). However, the neural bases of and connections between these two concepts are poorly understood. The present study hence used both questionnaires and a social optimism task performed during magnetic resonance imaging to investigate how network connectivity associates with personal and social optimism biases. Using sparse canonical correlation analysis, we found that a behavioral dimension that included both in-group optimism bias and personal optimism bias was positively associated with a dimension of network connectivity. This dimension comprised two networks with positive weights (dorsal precuneus-related default mode network and dorsal sensorimotor network), and three with negative weights (including parts of the salience and central executive networks). Our findings indicate that connectivity in networks adjacent to the temporoparietal junction favors propagation of both personal and social optimism biases. Meanwhile, low connectivity in more frontal networks associated with more complex cognition may also further such propagation.

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy.

Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.

Combined electrophysiological and morphological phenotypes in patients with genetic generalized epilepsy and their healthy siblings.

OBJECTIVE: Genetic generalized epilepsy (GGE) is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated whether these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence. METHODS: In this cross-sectional study, we analyzed 5 min of resting state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source-reconstructed power and connectivity in six frequency bands (1-40 Hz) and cortical thickness (derived from magnetic resonance imaging). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a nonparametric combination. RESULTS: Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls. SIGNIFICANCE: EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy.

Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data.

OBJECTIVE: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.

Reduced network integration in default mode and executive networks is associated with social and personal optimism biases.

An optimism bias refers to the belief in good things happening to oneself in the future with a higher likelihood than is justified. Social optimism biases extend this concept to groups that one identifies with. Previous literature has found that both personal and social optimism biases are linked to brain structure and task-related brain function. Less is known about whether optimism biases are also expressed in resting-state functional connectivity (RSFC). Forty-two participants completed questionnaires on dispositional personal optimism (which is not necessarily unjustified) and comparative optimism (i.e., whether we see our own future as being rosier than a comparison person's future) and underwent a resting-state functional magnetic resonance imaging scan. They further undertook an imaginative soccer task in order to assess both their personal and social optimism bias. We tested associations of these data with RSFC within and between 13 networks, using sparse canonical correlation analyses (sCCAs). We found that the primary sCCA component was positively connected to personal and social optimism bias and negatively connected to dispositional personal pessimism. This component was associated with (a) reduced integration of the default mode network, (b) reduced integration of the central executive and salience networks, and (c) reduced segregation between the default mode network and the central executive network. Our finding that optimism biases are linked to RSFC indicates that they may be rooted in neurobiology that exists outside of concurrent tasks. This poses questions as to what the limits of the malleability of such biases may be.

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

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

MP2RAGE multispectral voxel-based morphometry in focal epilepsy.

We assessed the applicability of MP2RAGE for voxel-based morphometry. To this end, we analyzed its brain tissue segmentation characteristics in healthy subjects and the potential for detecting focal epileptogenic lesions (previously visible and nonvisible). Automated results and expert visual interpretations were compared with conventional VBM variants (i.e., T1 and T1 + FLAIR). Thirty-one healthy controls and 21 patients with focal epilepsy were recruited. 3D T1-, T2-FLAIR, and MP2RAGE images (consisting of INV1, INV2, and MP2 maps) were acquired on a 3T MRI. The effects of brain tissue segmentation and lesion detection rates were analyzed among single- and multispectral VBM variants. MP2-single-contrast gave better delineation of deep, subcortical nuclei but was prone to misclassification of dura/vessels as gray matter, even more than conventional-T1. The addition of multispectral combinations (INV1, INV2, or FLAIR) could markedly reduce such misclassifications. MP2 + INV1 yielded generally clearer gray matter segmentation allowing better differentiation of white matter and neighboring gyri. Different models detected known lesions with a sensitivity between 60 and 100%. In non lesional cases, MP2 + INV1 was found to be best with a concordant rate of 37.5%, specificity of 51.6% and concordant to discordant ratio of 0.60. In summary, we show that multispectral MP2RAGE VBM (e.g., MP2 + INV1, MP2 + INV2) can improve brain tissue segmentation and lesion detection in epilepsy.

Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study.

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = -0.24 to -0.73; P < 1.49 × 10-4), and lower thickness in the precentral gyri bilaterally (d = -0.34 to -0.52; P < 4.31 × 10-6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = -1.73 to -1.91, P < 1.4 × 10-19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = -0.36 to -0.52; P < 1.49 × 10-4). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = -0.29 to -0.54; P < 1.49 × 10-4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = -0.27 to -0.51; P < 1.49 × 10-4). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < -0.0018; P < 1.49 × 10-4). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing.

Voxel-based morphometry is still mainly based on T1-weighted MRI scans. Misclassification of vessels and dura mater as gray matter has been previously reported. Goal of the present work was to evaluate the effect of multimodal segmentation methods available in SPM12, and their influence on identification of age related atrophy and lesion detection in epilepsy patients. 3D T1-, T2- and FLAIR-images of 77 healthy adults (mean age 35.8 years, 19-66 years, 45 females), 7 patients with malformation of cortical development (MCD) (mean age 28.1 years,19-40 years, 3 females), and 5 patients with left hippocampal sclerosis (LHS) (mean age 49.0 years, 25-67 years, 3 females) from a 3T scanner were evaluated. Segmentation based on T1-only, T1+T2, T1+FLAIR, T2+FLAIR, and T1+T2+FLAIR were compared in the healthy subjects. Clinical VBM results based on the different segmentation approaches for MCD and for LHS were compared. T1-only segmentation overestimated total intracranial volume by about 80ml compared to the other segmentation methods. This was due to misclassification of dura mater and vessels as GM and CSF. Significant differences were found for several anatomical regions: the occipital lobe, the basal ganglia/thalamus, the pre- and postcentral gyrus, the cerebellum, and the brainstem. None of the segmentation methods yielded completely satisfying results for the basal ganglia/thalamus and the brainstem. The best correlation with age could be found for the multimodal T1+T2+FLAIR segmentation. Highest T-scores for identification of LHS were found for T1+T2 segmentation, while highest T-scores for MCD were dependent on lesion and anatomical location. Multimodal segmentation is superior to T1-only segmentation and reduces the misclassification of dura mater and vessels as GM and CSF. Depending on the anatomical region and the pathology of interest (atrophy, lesion detection, etc.), different combinations of T1, T2 and FLAIR yield optimal results.

A preliminary study of the neuroanatomical correlates of primary writing tremor: role of cerebellum.

INTRODUCTION: To explore the neuroanatomical correlates of primary writing tremor (PWT) and the role of cerebellum, using advanced structural neuroimaging. Till date, there are no studies exploring the gray and white matter changes using voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) in PWT. METHODS: Ten male patients with PWT were evaluated clinically and with magnetic resonance imaging. VBM and DTI images of patients were compared with that of 10 healthy male subjects. Spatially unbiased infra-tentorial template (SUIT) analysis was done to investigate the alterations of cerebellar gray matter. Region-of-interest analysis was performed on regions observed to be significantly different on DTI analysis. RESULTS: The mean duration of illness and mean age of the patients were 3.5 ± 1.9 and 51.7 ± 8.6 years, respectively. On VBM analysis, the cluster of gray matter atrophy was found in bilateral cerebellar areas of culmen and left declive, right superior and medial frontal gyrus, bilateral middle frontal gyrus, bilateral anterior cingulate gyrus, and bilateral parahippocampal gyrus. DTI showed significantly reduced fractional anisotrophy of the anterior thalamic radiation, cingulum, and inferior fronto-occipital fasciculus in PWT patients compared to controls. The axial diffusivity, mean diffusivity, and radial diffusivity maps did not reveal any significant differences. On SUIT analysis, significant atrophy was found in right uvula and semilunar lobule in patients with PWT compared to controls. CONCLUSIONS: Our study found that patients with PWT had predominant gray matter atrophy in parts of cerebellum and frontal lobe along with white matter changes of the cingulum and frontal lobe connections.

A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Predictive modeling of optimism bias using gray matter cortical thickness.

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual's (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R2 = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex-areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being.

Multi-spectral diffusion MRI mega-analysis in genetic generalized epilepsy: Relation to outcomes.

BACKGROUND AND OBJECTIVES: Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes. METHODS: We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM). RESULTS: Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy. DISCUSSION: We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach.

ABSTRACT: Sensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging-based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.

Big Field of View MRI T1w and FLAIR Template - NMRI225.

Image templates are a common tool for neuroscience research. Often, they are used for spatial normalization of magnetic resonance imaging (MRI) data, which is a necessary procedure for analyzing brain morphology and function via voxel-based analysis. This allows the researcher to reduce individual shape differences across images and make inferences across multiple subjects. Many templates have a small field-of-view typically focussed on the brain, limiting the use for applications requiring detailed information about other extra-cranial structures in the head and neck area. However, there are several applications where such information is important, for example source reconstruction of electroencephalography (EEG) and/or magnetoencephalography (MEG). We have constructed a new template based on 225 T1w and FLAIR images with a big field-of-view that can serve both as target for across subject spatial normalization as well as a basis to build high-resolution head models. This template is based on and iteratively re-registered to the MNI152 space to provide maximal compatibility with the most commonly used brain MRI template.

Brain Structure and Optimism Bias: A Voxel-Based Morphometry Approach.

Individuals often anticipate an unrealistically favorable future for themselves (personal optimism bias) or others (social optimism bias). While such biases are well established, little is known about their neuroanatomy. In this study, participants engaged in a soccer task and estimated the likelihood of successful passes in personal and social scenarios. Voxel-based morphometry revealed that personal optimism bias varied as a positive function of gray matter volume (GMV) in the putamen, frontal pole, hippocampus, temporal pole, inferior temporal gyrus, visual association areas, and mid-superior temporal gyrus. Social optimism bias correlated positively with GMV in the temporoparietal junction and negatively with GMV in the inferior temporal gyrus and pre-supplementary motor areas. Together, these findings suggest that parts of our optimistic outlook are biologically rooted. Moreover, while the two biases looked similar at the behavioral level, they were related to distinct gray matter structures, proposing that their underlying mechanisms are not identical.

Whole-brain white matter correlates of personality profiles predictive of subjective well-being.

We investigated the white matter correlates of personality profiles predictive of subjective well-being. Using principal component analysis to first determine the possible personality profiles onto which core personality measures would load, we subsequently searched for whole-brain white matter correlations with these profiles. We found three personality profiles that correlated with the integrity of white matter tracts. The correlates of an "optimistic" personality profile suggest (a) an intricate network for self-referential processing that helps regulate negative affect and maintain a positive outlook on life, (b) a sustained capacity for visually tracking rewards in the environment and (c) a motor readiness to act upon the conviction that desired rewards are imminent. The correlates of a "short-term approach behavior" profile was indicative of minimal loss of integrity in white matter tracts supportive of lifting certain behavioral barriers, possibly allowing individuals to act more outgoing and carefree in approaching people and rewards. Lastly, a "long-term approach behavior" profile's association with white matter tracts suggests lowered sensitivity to transient updates of stimulus-based associations of rewards and setbacks, thus facilitating the successful long-term pursuit of goals. Together, our findings yield convincing evidence that subjective well-being has its manifestations in the brain.

Pregnancy and brain architecture: Associations with hormones, cognition and affect.

Sex hormones such as estradiol (E2) have long-lasting influence on brain architecture. Recent studies indicate further structural changes during hormonal transition periods including pregnancy, when women experience the greatest increase in sex hormone levels across their life span. In the present study, three groups of women (n = 44) with different levels of E2 underwent structural magnetic resonance imaging: (1) first-time pregnant women (n = 13, 'extreme E2 group'); (2), nulliparous, naturally cycling women who received 12 mg of E2 valerate (n = 16, 'high E2 group'); and (3) nulliparous, naturally cycling women receiving a placebo and hence low E2 (n = 15, 'low E2 group'). Blood samples were taken to assess hormonal levels. Moreover, parameters for cognition, emotion regulation and affect were assessed. On the neuronal level, the extreme E2 compared to the high E2 group showed a reduced gray matter volume in the left putamen. However, no significant differences were found between the low vs. high E2 groups, nor between the low E2 and extreme E2 groups. Cognitive performance was reduced in the extreme E2 group, although a positive affect was increased compared to the high E2 and low E2 groups. Furthermore, regression analyses revealed several associations between cognition, subjective measures of affect, emotion regulation and gray matter volume. A volume reduction of the left putamen during pregnancy further supports the notion that the female brain is shaped by hormonal transition phases, possibly preparing women for their future roles (e.g., pregnant women for their role as mothers).

Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression.

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.