ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Are Bipolar Disorder and Schizophrenia Neuroanatomically Distinct? An Anatomical Likelihood Meta-analysis

Objective: There is renewed debate on whether modern diagnostic classification should adopt a dichotomous or dimensional approach to schizophrenia and bipolar disorder. This study synthesizes data from voxel-based studies of schizophrenia and bipolar disorder to estimate the extent to which these conditions have a common neuroanatomical phenotype. Methods: A post-hoc meta-analytic estimation of the extent to which bipolar disorder, schizophrenia, or both conditions contribute to brain gray matter differences compared to controls was achieved using a novel application of the conventional anatomical likelihood estimation (ALE) method. 19 schizophrenia studies (651 patients and 693 controls) were matched as closely as possible to 19 bipolar studies (540 patients and 745 controls). Result: Substantial overlaps in the regions affected by schizophrenia and bipolar disorder included regions in prefrontal cortex, thalamus, left caudate, left medial temporal lobe, and right insula. Bipolar disorder and schizophrenia jointly contributed to clusters in the right hemisphere, but schizophrenia was almost exclusively associated with additional gray matter deficits (left insula and amygdala) in the left hemisphere. Limitation: The current meta-analytic method has a number of constraints. Importantly, only studies identifying differences between controls and patient groups could be included in this analysis. Conclusion: Bipolar disorder shares many of the same brain regions as schizophrenia. However, relative to neurotypical controls, lower gray matter volume in schizophrenia is more extensive and includes the amygdala. This fresh application of ALE accommodates multiple studies in a relatively unbiased comparison. Common biological mechanisms may explain the neuroanatomical overlap between these major disorders, but explaining why brain differences are more extensive in schizophrenia remains challenging

Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project.

BACKGROUND: Voxel-based morphometry (VBM) studies in autism spectrum disorder (autism) have yielded diverging results. This might partly be attributed to structural alterations being associating with the combined influence of several regions rather than with a single region. Further, these structural covariation differences may relate to continuous measures of autism rather than with categorical case-control contrasts. The current study aimed to identify structural covariation alterations in autism, and assessed canonical correlations between brain covariation patterns and core autism symptoms. METHODS: We studied 347 individuals with autism and 252 typically developing individuals, aged between 6 and 30 years, who have been deeply phenotyped in the Longitudinal European Autism Project. All participants' VBM maps were decomposed into spatially independent components using independent component analysis. A generalized linear model (GLM) was used to examine case-control differences. Next, canonical correlation analysis (CCA) was performed to separately explore the integrated effects between all the brain sources of gray matter variation and two sets of core autism symptoms. RESULTS: GLM analyses showed significant case-control differences for two independent components. The first component was primarily associated with decreased density of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and increased density of caudate nucleus in the autism group relative to typically developing individuals. The second component was related to decreased densities of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to typically developing individuals. The CCA results showed significant correlations between components that involved variation of thalamus, putamen, precentral gyrus, frontal, parietal, and occipital lobes, and the cerebellum, and repetitive, rigid and stereotyped behaviors and abnormal sensory behaviors in autism individuals. LIMITATIONS: Only 55.9% of the participants with autism had complete questionnaire data on continuous parent-reported symptom measures. CONCLUSIONS: Covaried areas associated with autism diagnosis and/or symptoms are scattered across the whole brain and include the limbic system, basal ganglia, thalamus, cerebellum, precentral gyrus, and parts of the frontal, parietal, and occipital lobes. Some of these areas potentially subserve social-communicative behavior, whereas others may underpin sensory processing and integration, and motor behavior

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

Structural and functional MRI study in mentally ill persons considered socially dangerous with diminished penal responsibility

La complessa relazione tra malattia mentale e criminalità rappresenta un tema di concreta rilevanza sociale, dibattuto da anni ma sempre di grande attualità. Secondo il codice penale, è considerato “socialmente pericoloso” il soggetto autore di reato, anche se non imputabile per vizio totale o parziale di mente, che abbia una elevata probabilità di recidiva del reato. Per questo motivo la prevenzione delle azioni socialmente pericolose riveste un ruolo di fondamentale importanza giuridica e sociale. In questo contesto la psichiatria forense si occupa delle questioni che sorgono all’interfaccia tra psichiatria e giurisprudenza, con l’obiettivo principale di evidenziare lo stato di salute mentale dei soggetti che commettono un reato attraverso una perizia psichiatrica. La disciplina neuroradiologica, grazie anche all’utilizzo di tecniche avanzate di analisi delle immagini, si pone oggi come strumento di valido ausilio nella valutazione clinica dei pazienti psichiatrici e può supportare gli sforzi congiunti di psichiatri e giuristi per studiare la relazione tra malattia mentale e criminalità. L’obiettivo di questo progetto di dottorato è stato quello di effettuare uno studio volumetrico della sostanza grigia cerebrale attraverso un esame di Risonanza Magnetica (RM) su un gruppo di soggetti autori di reato, considerati non imputabili al momento del fatto per vizio totale o parziale di mente, detenuti nella REMS dell’ASL Rm5 e considerati socialmente pericolosi. I risultati dell’analisi volumetrica sono stati confrontati con un gruppo di controllo, comparabile per età e sesso. E’stata inoltre effettuata un’analisi della connettività funzionale cerebrale a riposo (resting-state functional MRI) con l’intento di indagare i network cerebrali alla base del comportamento morale, dell’attribuzione della salienza e dei processi di ricompensa, confrontando sempre i risultati con un gruppo di controllo. Nel gruppo sperimentale sono stati inclusi 13 individui destrorsi (età media: 44 ± 7 anni) detenuti nella REMS dell’ASL Rm5 con disturbo dello spettro psicotico (schizofrenia, disturbo bipolare con caratteristiche psicotiche, disturbo schizo-affettivo, disturbi deliranti), che hanno commesso crimini violenti (omicidi, tentati omicidi, aggressioni e violenze domestiche) e che sono stati dichiarati socialmente pericolosi dall’autorità giudiziaria a causa dell’ alto rischio di recidiva criminale. I dati di RM sono stati acquisiti su un magnete 3 Tesla (Verio, Siemens) dotato di una bobina a 12 canali, utilizzando sequenze volumetriche T13D e sequenze BOLD eco-planari (EPI). Nello studio I abbiamo eseguito un’analisi della volumetria cerebrale con tecnica VBM (Voxel-based morphometry) utilizzando il Computational Anatomy Toolbox (CAT12) del software Statistical Parametric Mapping (SPM12). Abbiamo riscontrato come il volume della sostanza grigia cerebrale del gruppo sperimentale fosse significativamente ridotto, rispetto ai controlli, a livello della corteccia insulare bilaterale, nel giro temporale superiore (STG) dell’emisfero sinistro e nel giro fusiforme dell’emisfero destro. Abbiamo infine eseguito un’analisi di correlazione tra la gravità dei sintomi psichiatrici e le regioni con volume corticale ridotto. I cluster di volume a livello di STG e insula sinistra sono risultati essere significativamente correlati alla gravità dei sintomi espressa dalla scala di valutazione BPRS (Brief Psychiatric Rating Scale). Nello studio II abbiamo esaminato la connettività cerebrale a riposo nelle 19 regioni selezionate “a priori” sulla base della letteratura che risultassero coinvolte nella morale, nell’ attribuzione della salienza e nei processi di ricompensa. L’analisi è stata effettuata utilizzando il software CONN v. 18a, sulla piattaforma Matlab. Abbiamo documentato una ridotta connettività tra le regioni del sistema limbico, come il nucleo accumbens e l’amigdala, ed aumentata connettività nello striato dorsale, tra il nucleo accumbens e la corteccia cingolata posteriore, tra corteccia fronto-orbitale e gangli della base e tra corteccia cingolata anteriore e amigdala. Sulla base di questi risultati ipotizziamo che l’alterata connettività in queste specifiche aree possa rappresentare la modificazione del comportamento in senso maladattativo degli individui del gruppo sperimentale, in termini di alterata risposta emotiva circa le proprie violazioni morali o di mancanza di empatia verso gli altri al fine di ottenere vantaggi personali o riguardo al controllo dell’impulsività. Nonostante la bassa numerosità campionaria non consenta di approdare a conclusioni definitive, questo studio cerca di approfondire i correlati neurali degli individui autori di reato con ridotta responsabilità penale e socialmente pericolosi al fine di fornire un eventuale strumento di ausilio nella valutazione di questa particolare categoria di persone, con importanti risvolti giuridici ed etici oltre che nella pianificazione e nello sviluppo del trattamento di questi pazienti durante la loro permanenza nelle REMS.The relation between mental illness and criminality is a relevant social issue that has been debated over the years. Socially dangerous actions committed by mentally ill patients often have severe consequences, which is why much public attention is directed toward the prevention of these actions by these individuals. Modern neuroimaging investigations support the joint efforts of psychiatrists and lawyers to study the relationship between psychiatric illness and criminality. The overall aim of this PhD project was to investigate differences in cortical GM volumes of this population, compared to a control group of healthy non-offender participants, using a VBM analysis of structural MRI. We also decided to investigate brain networks underpinning moral behaviour, salience attribution and reward processes performing a functional MRI at resting-state. Experimental Group (EG) included 13 right-handed individuals (mean age: 44 ± 7 yrs) who committed violent crimes (homicides, attempted homicides, aggressions, and domestic violence), had a diagnosis included in the psychotic spectrum (schizophrenia, bipolar disorder with psychotic features, schizoaffective disorder, delusional disorders) and were declared socially dangerous by the judicial authority due to a high risk of criminal recidivism. All subjects of the EG were institutionalized in the REMS psychiatric unit of ASL RM5 (Rome, Italy) for no longer than two years. Thirteen healthy right-handed men, who had never received a psychiatric diagnosis, undergone any psychiatric treatment, or been convicted of any crime were included in the control group (CG) (mean age: 38 ± 11yrs). MRI data were acquired using a 3 Tesla Siemens imaging system (Siemens, Verio, Erlangen, Germany) equipped with a 12-channel head coil. Structural scans of the brain were acquired for each participant using a T1-weighted three dimensionals sagittal magnetization-prepared rapid gradient echo sequence. Resting state functional (rs-fMRI) data were collected while participants lay still and awake, with eyes closed using T2*-weighted gradient-echo echo-planar functional images (EPIs). In study I we performed a voxel-based morphometry (VBM) analyses on participants’ T1-weighted structural images using Computational Anatomy Toolbox (CAT12), which runs within SPM12. We found that total cerebral GM volume was significantly reduced in EG in specific regions within the bilateral insular cortex compared to controls. We also found a reduced GM volume in the superior temporal gyrus (STG) of left hemisphere and in the fusiform gyrus of the right hemisphere. We finally performed a correlation analyses between psychiatric symptoms and regions with reduced GM volume. The clusters in STG and insula of left hemisphere significantly correlated with the gravity of symptoms expressed by the BPRS (Brief Psychiatric Rating Scale). In study II, temporal correlations of the resting-state BOLD signal time series were examined between nineteen seed regions that we selected “a priori” among those known to be involved in moral judgment salience attribution and reward processes. Analysis was performed using the software CONN v. 18a, running in Matlab. Our results documented reduced connectivity in limbic regions like the nucleus accumbens and the amiygdala and augmented connectivity within the dorsal striatum, between nucleus accumbens and the posterior cingulate cortex, between fronto- orbitalis cortex and basal ganglia and anterior cingulate cortex and amygdala. We suggest that dysregulation in these areas reflects the maladaptive behavior of socially dangerous subjects in terms of an altered emotional response to their own moral violations and a lack of empathy for others when making personal desire-oriented decisions. While the small sample size does not allow definitive conclusions to be reached, the present study sheds some light on the neural correlates of this specific population, which deserves further attention due to their theoretical and clinical implications. A further understanding of the neural basis of risk evaluation in mentally ill persons with a history of violence who are judged not criminally responsible could aid in forensic assessment and treatment development

SEARCHING NEUROIMAGING BIOMARKERS IN MENTAL DISORDERS WITH GRAPH AND MULTIMODAL FUSION ANALYSIS OF FUNCTIONAL CONNECTIVITY

Mental disorders such as schizophrenia (SZ), bipolar (BD), and major depression disorders (MDD) can cause severe symptoms and life disruption. They share some symptoms, which can pose a major clinical challenge to their differentiation. Objective biomarkers based on neuroimaging may help to improve diagnostic accuracy and facilitate optimal treatment for patients. Over the last decades, non-invasive in-vivo neuroimaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure structure and function in human brains. With functional MRI (fMRI) or structural MRI (sMRI), studies have identified neurophysiological deficits in patients’ brain from different perspective. Functional connectivity (FC) analysis is an approach that measures functional integration in brains. By assessing the temporal coherence of the hemodynamic activity among brain regions, FC is considered capable of characterizing the large-scale integrity of neural activity. In this work, we present two data analysis frameworks for biomarker detection on brain imaging with FC, 1) graph analysis of FC and 2) multimodal fusion analysis, to better understand the human brain. Graph analysis reveals the interaction among brain regions based on graph theory, while the multimodal fusion framework enables us to utilize the strength of different imaging modalities through joint analysis. Four applications related to FC using these frameworks were developed. First, FC was estimated using a model-based approach, and revealed altered the small-world network structure in SZ. Secondly, we applied graph analysis on functional network connectivity (FNC) to differentiate BD and MDD during resting-state. Thirdly, two functional measures, FNC and fractional amplitude of low frequency fluctuations (fALFF), were spatially overlaid to compare the FC and spatial alterations in SZ. And finally, we utilized a multimodal fusion analysis framework, multi-set canonical correlation analysis + joint independent component analysis (mCCA+jICA) to link functional and structural abnormalities in BD and MDD. We also evaluated the accuracy of predictive diagnosis through classifiers generated on the selected features. In summary, via the two frameworks, our work has made several contributions to advance FC analysis, which improves our understanding of underlying brain function and structure, and our findings may be ultimately useful for the development of biomarkers of mental disease

Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA

Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called “mCCA + jICA” as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ

The Identification of Genes and Brain Patterns in the Quantitative Trait Loci of Chromosome 5

In previous research, Gupta et al. (2015) analyzed gray matter density as well as volume reductions related to schizophrenia in the region of the insula and medial prefrontal cortex. Sprooten et al. (2015) then identified a set of quantitative trait loci (QTLs), which is a region of DNA associated with variability in these gray matter concentration patterns. The aim of this study is to examine the QTL they found in a region of chromosome 5. We hypothesized that there will be a set of genes in the QTL on chromosome 5 that is related to abnormal brain patterns in potential disorders such as schizophrenia. We identified genes present in the region of the QTL to analyze their function and relatedness to other genes using various software like Ingenuity Pathways Analysis, and Gene Cards. We evaluated their biological functions as well as any related disorders. For the imaging and genetic analyses, the genotypic data contained 9,228 single-nucleotide polymorphisms (SNPs) from shared aggregated datasets. The datasets contained clinical information for 616 subjects (364 controls, 252 cases). Each subject had a corresponding brain image. We identified a set of genes, including SLC1A3, GDNF, C6, C7, and C9, that are possibly related to neurodegeneration as well as brain injury processes. Lastly, we employed the parallel independent component analysis technique (pICA) to incorporate the genetic data with brain imaging to possibly identify an area related to schizophrenia. Some of the genetic variations found corresponded to the genes C7, RPL37, and PTGER4 with a correlation of 0.1012. C7, RPL37, and PTGER4 are involved in the immune system, multiple sclerosis, and neurodegenerative diseases. These genes were correlated with the imaging pattern from the pICA in the regions of the cerebellum, vermis, and mid-temporal lobe. Further analyses are needed to evaluate the correlation obtained from the pICA

Post-mortem analysis of cortical thickness and neuronal morphometry in autism.

Autism Spectrum Disorders (ASD) are a group of conditions characterized by a broad spectrum of deficiencies related to social communication, both verbal and non-verbal, as well as repetitive behaviors such as rocking back and forth, or arm flapping. This devastating group of disorders affects millions of people regardless of their socioeconomic, ethnic and racial backgrounds. Among other issues such as migraines, sensory deficits and intense aggression, epilepsy is a major neurological disorder very highly associated with ASD; roughly one third of all ASD patients are diagnosed with epilepsy, and a much higher proportion suffer from at least one seizure in their lifetime. Past neuropathological studies have suggested various deformities in the brains of ASD patients, ranging from abnormal brain volume, and head circumference, to the presence of atrophy and cortical dysplasias, such as heterotopias. Other more cellular abnormalities have also been speculated, including neurotransmitter imbalances, as well as neuron proliferation and organization disturbances. Though many intricate techniques have been attempted in order to investigate the etiology and potential treatments of ASD pathology, the results have been inconclusive and highly arguable. This especially applies to magnetic resonance imaging (MRI) studies of cortical thickness, which have produced highly questionable and inconsistent information; researchers have attributed this to the potential instability of the mode of research. Due to the limitations on the resolution of MRI, it is difficult to directly compute the location of the pial surface, which creates difficulty in delineating the cortical gray matter from the subcortical white matter. The goal of our study was to indentify cortical dysplasias, and then to analyze the neuromorphology of the same areas. In order to accomplish this, we retrieved post-mortem tissue from the Autism Tissue Program, and measured the cortical thickness by solving the Laplace equation, followed by the application of the Boolean model and granulometry to determine the potential abnormalities of ASD neuronal morphometry. We found multiple dysplasias in various brain regions, predominantly within the pre-frontal cortex, which correlated nicely with the symptomology of ASD. The anterior commissure (AC) served as the landmark to delineate the prefrontal cortex; when compared with neurotypical tissue, the ASD tissue was thinner surrounding the AC. Upon further analysis of the dysplastic processes, our findings confirmed the presence of smaller, but more numerous pyramidal neurons in the ASD brain when compared with neurotypicals. These findings are also substantial in terms of explaining two of the more prominent issues associated with ASD: aggressive behavior and frequent seizures. Overall, the findings of the current study do support several previous reports and provides further evidence of problematic cortical development and ASD symptom manifestation

Source‐based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1‐weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source‐based morphometry (SBM) pipeline (SS‐Detect) to generate structural brain patterns (SBPs) that capture co‐varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV‐SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel‐based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism