White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

Cortical Macro- and Microstructural Changes in Parkinson's Disease with Probable Rapid Eye Movement Sleep Behavior Disorder.

BACKGROUND: Evidence regarding cortical atrophy patterns in Parkinson's disease (PD) with probable rapid eye movement sleep behavior disorder (RBD) (PD-pRBD) remains scarce. Cortical mean diffusivity (cMD), as a novel imaging biomarker highly sensitive to detecting cortical microstructural changes in different neurodegenerative diseases, has not been investigated in PD-pRBD yet. OBJECTIVES: The aim was to investigate cMD as a sensitive measure to identify subtle cortical microstructural changes in PD-pRBD and its relationship with cortical thickness (CTh). METHODS: Twenty-two PD-pRBD, 31 PD without probable RBD (PD-nonpRBD), and 28 healthy controls (HC) were assessed using 3D T1-weighted and diffusion-weighted magnetic resonance imaging on a 3-T scanner and neuropsychological testing. Measures of cortical brain changes were obtained through cMD and CTh. Two-class group comparisons of a general linear model were performed (P < 0.05). Cohen's d effect size for both approaches was computed. RESULTS: PD-pRBD patients showed higher cMD than PD-nonpRBD patients in the left superior temporal, superior frontal, and precentral gyri, precuneus cortex, as well as in the right middle frontal and postcentral gyri and paracentral lobule (d > 0.8), whereas CTh did not detect significant differences. PD-pRBD patients also showed increased bilateral posterior cMD in comparison with HCs (d > 0.8). These results partially overlapped with CTh results (0.5 < d < 0.8). PD-nonpRBD patients showed no differences in cMD when compared with HCs but showed cortical thinning in the left fusiform gyrus and lateral occipital cortex bilaterally (d > 0.5). CONCLUSIONS: cMD may be more sensitive than CTh displaying significant cortico-structural differences between PD subgroups, indicating this imaging biomarker's utility in studying early cortical changes in PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

OBJECTIVE: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. METHODS: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. RESULTS: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( η ρ max 2 = .05) and longer epilepsy duration ( η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SIGNIFICANCE: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.

Non-invasive prehabilitation to foster widespread fMRI cortical reorganization before brain tumor surgery: lessons from a case series.

PURPOSE: The objective of this prospective, single-centre case series was to investigate feasibility, clinical outcomes, and neural correlates of non-invasive Neuromodulation-Induced Cortical Prehabilitation (NICP) before brain tumor surgery. Previous studies have shown that gross total resection is paramount to increase life expectancy but is counterbalanced by the need of preserving critical functional areas. NICP aims at expanding functional margins for extensive tumor resection without functional sequelae. Invasive NICP (intracranial neuromodulation) was effective but characterized by elevated costs and high rate of adverse events. Non-invasive NICP (transcranial neuromodulation) may represent a more feasible alternative. Nonetheless, up to this point, non-invasive NICP has been examined in only two case reports, yielding inconclusive findings. METHODS: Treatment sessions consisted of non-invasive neuromodulation, to transiently deactivate critical areas adjacent to the lesion, coupled with intensive functional training, to activate alternative nodes within the same functional network. Patients were evaluated pre-NICP, post-NICP, and at follow-up post-surgery. RESULTS: Ten patients performed the intervention. Feasibility criteria were met (retention, adherence, safety, and patient's satisfaction). Clinical outcomes showed overall stability and improvements in motor and executive function from pre- to post-NICP, and at follow-up. Relevant plasticity changes (increase in the distance between tumor and critical area) were observed when the neuromodulation target was guided by functional neuroimaging data. CONCLUSION: This is the first case series demonstrating feasibility of non-invasive NICP. Neural correlates indicate that neuroimaging-guided target selection may represent a valid strategy to leverage neuroplastic changes before neurosurgery. Further investigations are needed to confirm such preliminary findings.

Cortical microinfarcts in adults with Down syndrome assessed with 3T-MRI.

BACKGROUND: Cortical microinfarcts (CMI) were attributed to cerebrovascular disease and cerebral amyloid angiopathy (CAA). CAA is frequent in Down syndrome (DS) while hypertension is rare, yet no studies have assessed CMI in DS. METHODS: We included 195 adults with DS, 63 with symptomatic sporadic Alzheimer's disease (AD), and 106 controls with 3T magnetic resonance imaging. We assessed CMI prevalence in each group and CMI association with age, AD clinical continuum, vascular risk factors, vascular neuroimaging findings, amyloid/tau/neurodegeneration biomarkers, and cognition in DS. RESULTS: CMI prevalence was 11.8% in DS, 4.7% in controls, and 17.5% in sporadic AD. In DS, CMI increased in prevalence with age and the AD clinical continuum, was clustered in the parietal lobes, and was associated with lacunes and cortico-subcortical infarcts, but not hemorrhagic lesions. DISCUSSION: In DS, CMI are posteriorly distributed and related to ischemic but not hemorrhagic findings suggesting they might be associated with a specific ischemic CAA phenotype. HIGHLIGHTS: This is the first study to assess cortical microinfarcts (assessed with 3T magnetic resonance imaging) in adults with Down syndrome (DS). We studied the prevalence of cortical microinfarcts in DS and its relationship with age, the Alzheimer's disease (AD) clinical continuum, vascular risk factors, vascular neuroimaging findings, amyloid/tau/neurodegeneration biomarkers, and cognition. The prevalence of cortical microinfarcts was 11.8% in DS and increased with age and along the AD clinical continuum. Cortical microinfarcts were clustered in the parietal lobes, and were associated with lacunes and cortico-subcortical infarcts, but not hemorrhagic lesions. In DS, cortical microinfarcts are posteriorly distributed and related to ischemic but not hemorrhagic findings suggesting they might be associated with a specific ischemic phenotype of cerebral amyloid angiopathy.

Locus coeruleus integrity and neuropsychiatric symptoms in a cohort of early- and late-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) shows a higher burden of neuropsychiatric symptoms than late-onset Alzheimer's disease (LOAD). We aim to determine the differences in the severity of neuropsychiatric symptoms and locus coeruleus (LC) integrity between EOAD and LOAD accounting for disease stage. METHODS: One hundred four subjects with AD diagnosis and 32 healthy controls were included. Participants underwent magnetic resonance imaging (MRI) to measure LC integrity, measures of noradrenaline levels in cerebrospinal fluid (CSF) and Neuropsychiatric Inventory (NPI). We analyzed LC-noradrenaline measurements and clinical and Alzheimer's disease (AD) biomarker associations. RESULTS: EOAD showed higher NPI scores, lower LC integrity, and similar levels of CSF noradrenaline compared to LOAD. Notably, EOAD exhibited lower LC integrity independently of disease stage. LC integrity negatively correlated with neuropsychiatric symptoms. Noradrenaline levels were increased in AD correlating with AD biomarkers. DISCUSSION: Decreased LC integrity negatively contributes to neuropsychiatric symptoms. The higher LC degeneration in EOAD compared to LOAD could explain the more severe neuropsychiatric symptoms in EOAD. HIGHLIGHTS: LC degeneration is greater in early-onset AD (EOAD) compared to late-onset AD. Tau-derived LC degeneration drives a higher severity of neuropsychiatric symptoms. EOAD harbors a more profound selective vulnerability of the LC system. LC degeneration is associated with an increase of cerebrospinal fluid noradrenaline levels in AD.

Characterization of white matter hyperintensities in Down syndrome.

INTRODUCTION: In Down syndrome (DS), white matter hyperintensities (WMHs) are highly prevalent, yet their topography and association with sociodemographic data and Alzheimer's disease (AD) biomarkers remain largely unexplored. METHODS: In 261 DS adults and 131 euploid controls, fluid-attenuated inversion recovery magnetic resonance imaging scans were segmented and WMHs were extracted in concentric white matter layers and lobar regions. We tested associations with AD clinical stages, sociodemographic data, cerebrospinal fluid (CSF) AD biomarkers, and gray matter (GM) volume. RESULTS: In DS, total WMHs arose at age 43 and showed stronger associations with age than in controls. WMH volume increased along the AD continuum, particularly in periventricular regions, and frontal, parietal, and occipital lobes. Associations were found with CSF biomarkers and temporo-parietal GM volumes. DISCUSSION: WMHs increase 10 years before AD symptom onset in DS and are closely linked with AD biomarkers and neurodegeneration. This suggests a direct connection to AD pathophysiology, independent of vascular risks. HIGHLIGHTS: White matter hyperintensities (WMHs) increased 10 years before Alzheimer's disease symptom onset in Down syndrome (DS). WMHs were strongly associated in DS with the neurofilament light chain biomarker. WMHs were more associated in DS with gray matter volume in parieto-temporal areas.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

Age-related brain deviations and aggression.

BACKGROUND: Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities. METHODS: We combined two novel approaches. First, normative modeling to map deviations from the typical age-related pattern at the level of the individual of (i) activity during emotion matching and (ii) of anatomical images derived from DBD cases (n = 77) and controls (n = 52) aged 8-18 years from the EU-funded Aggressotype and MATRICS consortia. Second, linked independent component analysis to integrate subject-specific deviations from both modalities. RESULTS: While cases exhibited on average a higher activity than would be expected for their age during face processing in regions such as the amygdala when compared to controls these positive deviations were widespread at the individual level. A multimodal integration of all functional and anatomical deviations explained 23% of the variance in the clinical DBD phenotype. Most notably, the top marker, encompassing the default mode network (DMN) and subcortical regions such as the amygdala and the striatum, was related to aggression across the whole sample. CONCLUSIONS: Overall increased age-related deviations in the amygdala in DBD suggest a maturational delay, which has to be further validated in future studies. Further, the integration of individual deviation patterns from multiple imaging modalities allowed to dissect some of the heterogeneity of DBD and identified the DMN, the striatum and the amygdala as neural signatures that were associated with aggression.

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

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

Psychiatric and psychological assessment of Spanish patients with drug-resistant epilepsy and psychogenic nonepileptic seizures (PNES) with no response to previous treatments.

OBJECTIVE: Psychogenic nonepileptic seizures (PNES) are common imitators of epileptic seizures. Refractoriness to antiseizure medication hinders the differential diagnosis between ES and PNES, carrying deleterious consequences in patients with PNES. Psychiatric and psychological characteristics may assist in the differential diagnosis between drug-resistant epilepsy (DRE) and PNES. Nevertheless, current comprehensive psychiatric and psychological descriptive studies on both patient groups are scarce and with several study limitations. This study provides a comprehensive psychiatric and psychological characterization of Spanish patients with DRE and PNES. METHOD: A cross-sectional and comparative study was completed with 104 patients with DRE and 21 with PNES. Psychiatric and psychological characteristics were assessed with the HADS, SCL-90-R, NEO-FFI-R, PDQ-4+, COPE, and QOLIE-31 tests. Parametric and non-parametric tests were used, and regression models were fit to further explore factors affecting patients' life quality. RESULTS: Patients with PNES had greater levels of somatization and extraversion and were associated with benzodiazepine intake. Patients with DRE showed greater narcissistic personality disorder symptoms than those with PNES. In patients with DRE, difficulty in performing basic needs-related tasks and greater psychological distress severity and seizure frequency were associated with poorer life quality. In contrast, being a woman, having a psychiatric disorder history, and greater psychiatric symptoms' intensity were associated with poorer life quality in patients with PNES. CONCLUSION: Patients with DRE and PNES share similar psychiatric and psychological characteristics, with only very few being significantly different.

Longitudinal study in adolescent anorexia nervosa: evaluation of cortico-striatal and default mode network resting-state brain circuits.

Anorexia nervosa (AN) typically emerges in adolescence. The cortico-striatal system (CSTS) and the default mode network (DMN) are brain circuits with a crucial development during this period. These circuits underlie cognitive functions that are impaired in AN, such as cognitive flexibility and inhibition, among others. Little is known about their involvement in adolescent AN and how weight and symptom improvement might modulate potential alterations in these circuits. Forty-seven adolescent females (30 AN, 17 healthy control) were clinically/neuropsychologically evaluated and scanned during a 3T-MRI resting-state session on two occasions, before and after a 6-month multidisciplinary treatment of the AN patients. Baseline and baseline-to-follow-up between-group differences in CSTS and DMN resting-state connectivity were evaluated, as well as their association with clinical/neuropsychological variables. Increased connectivity between the left dorsal putamen and the left precuneus was found in AN at baseline. At follow-up, body mass index and clinical symptoms had improved in the AN group. An interaction effect was found in the connectivity between the right dorsal caudate to right mid-anterior insular cortex, with lower baseline AN connectivity that improved at follow-up; this improvement was weakly associated with changes in neuropsychological (Stroop test) performance. These results support the presence of CSTS connectivity alterations in adolescents with AN, which improve with weight and symptom improvement. In addition, at the level of caudate-insula connectivity, they might be associated with inhibitory processing performance. Alterations in CSTS pathways might be involved in AN from the early stages of the disorder.

Basal forebrain atrophy along the Alzheimer's disease continuum in adults with Down syndrome.

BACKGROUND: Basal forebrain (BF) degeneration occurs in Down syndrome (DS)-associated Alzheimer's disease (AD). However, the dynamics of BF atrophy with age and disease progression, its impact on cognition, and its relationship with AD biomarkers have not been studied in DS. METHODS: We included 234 adults with DS (150 asymptomatic, 38 prodromal AD, and 46 AD dementia) and 147 euploid controls. BF volumes were extracted from T-weighted magnetic resonance images using a stereotactic atlas in SPM12. We assessed BF volume changes with age and along the clinical AD continuum and their relationship to cognitive performance, cerebrospinal fluid (CSF) and plasma amyloid/tau/neurodegeneration biomarkers, and hippocampal volume. RESULTS: In DS, BF volumes decreased with age and along the clinical AD continuum and significantly correlated with amyloid, tau, and neurofilament light chain changes in CSF and plasma, hippocampal volume, and cognitive performance. DISCUSSION: BF atrophy is a potentially valuable neuroimaging biomarker of AD-related cholinergic neurodegeneration in DS.

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.

Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

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.

A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies.

AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

The effects of callous-unemotional traits and aggression subtypes on amygdala activity in response to negative faces.

BACKGROUND: Brain imaging studies have shown altered amygdala activity during emotion processing in children and adolescents with oppositional defiant disorder (ODD) and conduct disorder (CD) compared to typically developing children and adolescents (TD). Here we aimed to assess whether aggression-related subtypes (reactive and proactive aggression) and callous-unemotional (CU) traits predicted variation in amygdala activity and skin conductance (SC) response during emotion processing. METHODS: We included 177 participants (n = 108 cases with disruptive behaviour and/or ODD/CD and n = 69 TD), aged 8-18 years, across nine sites in Europe, as part of the EU Aggressotype and MATRICS projects. All participants performed an emotional face-matching functional magnetic resonance imaging task. RESULTS: Differences between cases and TD in affective processing, as well as specificity of activation patterns for aggression subtypes and CU traits, were assessed. Simultaneous SC recordings were acquired in a subsample (n = 63). Cases compared to TDs showed higher amygdala activity in response to negative faces (fearful and angry) v. shapes. Subtyping cases according to aggression-related subtypes did not significantly influence on amygdala activity; while stratification based on CU traits was more sensitive and revealed decreased amygdala activity in the high CU group. SC responses were significantly lower in cases and negatively correlated with CU traits, reactive and proactive aggression. CONCLUSIONS: Our results showed differences in amygdala activity and SC responses to emotional faces between cases with ODD/CD and TD, while CU traits moderate both central (amygdala) and peripheral (SC) responses. Our insights regarding subtypes and trait-specific aggression could be used for improved diagnostics and personalized treatment.