Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Feasibility of common, enjoyable game play for assessing daily cognitive functioning in older adults.

Background: Frequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer's disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack© for recurrent semi-passive assessment of cognition across a longitudinal interval. Methods: The MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match. Participants were asked to play 20 min/day for 5 days (100 min) for 4 months. Feasibility of use by older adults was assessed by measuring gameplay time and game performance. We also evaluated compliance through semi-structured surveys. A linear generalized estimating equation (GEE) model was used to analyze changes in gameplay time, and a regression tree model was employed to estimate the days it took for game performance to plateau. Subjective and environmental factors associated with gameplay time and performance were examined, including daily self-reported questions of memory and thinking ability, mood, sleep, energy, current location, and distractions prior to gameplay. Results: Twenty-six cognitively-unimpaired older adults participated (mean age ± SD = 71.9 ± 8.6; 73% female). Gameplay time remained stable throughout the 4-months, with an average compliance rate of 91% ± 11% (1946 days of data across all participants) and weekly average playtime of 210 ± 132 min per participant. We observed an initial learning curve of improving game performance which on average, plateaued after 22-39 days, depending on the game. Higher levels of self-reported memory and thinking ability were associated with more gameplay time and sessions. Conclusion: MIND GamePack is a feasible and well-designed semi-passive cognitive assessment platform which may provide complementary data to traditional neuropsychological testing in research on aging and dementia.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia.

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Federated Analysis of Neuroimaging Data: A Review of the Field.

The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging data.

Modular and state-relevant functional network connectivity in high-frequency eyes open vs eyes closed resting fMRI data.

BACKGROUND: Resting-state fMRI (rs-fMRI) is employed to assess "functional connections" of signal between brain regions. However, multiple rs-fMRI paradigms and data-filtering strategies have been used, highlighting the need to explore BOLD signal across the spectrum. Rs-fMRI data is typically filtered at frequencies ranging between 0.008∼0.2 Hz to mitigate nuisance signal (e.g. cardiac, respiratory) and maximize neuronal BOLD signal. However, some argue neuronal BOLD signal may be parsed at higher frequencies. NEW METHOD: To assess the contributions of rs-fMRI along the BOLD spectra on functional network connectivity (FNC) matrices, a spatially constrained independent component analysis (ICA) was performed at seven different frequency "bins", after which FNC values and FNC measures of matrix-randomness were assessed using linear mixed models. RESULTS: Results show FNCs at higher-frequency bins display similar randomness to those from the typical frequency bins (0.01-0.15), while the largest values are in the 0.31-0.46 Hz bin. Eyes open (EO) vs eyes closed (EC) comparison found EC was less random than EO across most frequency bins. Further, FNC was greater in EC across auditory and cognitive control pairings while EO values were greater in somatomotor, visual, and default mode FNC. COMPARISON WITH EXISTING METHODS: Effect sizes of frequency and resting-state paradigm vary from small to large, but the most notable results are specific to frequency ranges and resting-state paradigm with artifacts like motion displaying negligible effect sizes. CONCLUSIONS: These suggest unique information may be derived from FNC matrices across frequencies and paradigms, but additional data is necessary prior to any definitive conclusions.

Common and unique multimodal covarying patterns in autism spectrum disorder subtypes.

BACKGROUND: The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD. METHODS: In this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective. The Autism Diagnostic Observation Schedule (ADOS) was used as a reference to guide functional and structural MRI fusion. DSM-IV-TR diagnosed Asperger's disorder (n = 79), pervasive developmental disorder-not otherwise specified [PDD-NOS] (n = 58) and Autistic disorder (n = 92) from ABIDE II were used as discovery cohort, and ABIDE I (n = 400) was used for replication. RESULTS: Dorsolateral prefrontal cortex and superior/middle temporal cortex are the primary common functional-structural covarying cortical brain areas shared among Asperger's, PDD-NOS and Autistic subgroups. Key differences among the three subtypes are negative functional features within subcortical brain areas, including negative putamen-parahippocampus fractional amplitude of low-frequency fluctuations (fALFF) unique to the Asperger's subtype; negative fALFF in anterior cingulate cortex unique to PDD-NOS subtype; and negative thalamus-amygdala-caudate fALFF unique to the Autistic subtype. Furthermore, each subtype-specific brain pattern is correlated with different ADOS subdomains, with social interaction as the common subdomain. The identified subtype-specific patterns are only predictive for ASD symptoms manifested in the corresponding subtypes, but not the other subtypes. CONCLUSIONS: Although ASD has a common neural basis with core deficits linked to social interaction, each ASD subtype is strongly linked to unique brain systems and subdomain symptoms, which may help to better understand the underlying mechanisms of ASD heterogeneity from a multimodal neuroimaging perspective. LIMITATIONS: This study is male based, which cannot be generalized to the female or the general ASD population.

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study.

Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.

Reproducibility of whole-brain temperature mapping and metabolite quantification using proton magnetic resonance spectroscopy.

Assessing brain temperature can provide important information about disease processes (e.g., stroke, trauma) and therapeutic effects (e.g., cerebral hypothermia treatment). Whole-brain magnetic resonance spectroscopic imaging (WB-MRSI) is increasingly used to quantify brain metabolites across the entire brain. However, its feasibility and reliability for estimating brain temperature needs further validation. Therefore, the present study evaluates the reproducibility of WB-MRSI for temperature mapping as well as metabolite quantification across the whole brain in healthy volunteers. Ten healthy adults were scanned on three occasions 1 week apart. Brain temperature, along with four commonly assessed brain metabolites-total N-acetyl-aspartate (tNAA), total creatine (tCr), total choline (tCho) and myo-inositol (mI)-were measured from WB-MRSI data. Reproducibility was evaluated using the coefficient of variation (CV). The measured mean (range) of the intra-subject CVs was 0.9% (0.6%-1.6%) for brain temperature mapping, and 4.7% (2.5%-15.7%), 6.4% (2.4%-18.9%) and 14.2% (4.4%-52.6%) for tNAA, tCho and mI, respectively, with reference to tCr. Consistently larger variability was found when using H2 O as the reference for metabolite quantifications: 7.8% (3.3%-17.8%), 7.8% (3.1%-18.0%), 9.8% (3.7%-31.0%) and 17.0% (5.9%-54.0%) for tNAA, tCr, tCho and mI, respectively. Further, the larger the brain region (indicated by a greater number of voxels within that region), the better the reproducibility for both temperature and metabolite estimates. Our results demonstrate good reproducibility of whole-brain temperature and metabolite measurements using the WB-MRSI technique.

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder.

Social impairments in autism spectrum disorder (ASD), a hallmark feature of its diagnosis, may underlie specific neural signatures that can aid in differentiating between those with and without ASD. To assess common and consistent patterns of differences in brain responses underlying social cognition in ASD, this study applied an activation likelihood estimation (ALE) meta-analysis to results from 50 neuroimaging studies of social cognition in children and adults with ASD. In addition, the group ALE clusters of activation obtained from this was used as a social brain mask to perform surface-based cortical morphometry (SBM) in an empirical structural MRI dataset collected from 55 ASD and 60 typically developing (TD) control participants. Overall, the ALE meta-analysis revealed consistent differences in activation in the posterior superior temporal sulcus at the temporoparietal junction, middle frontal gyrus, fusiform face area (FFA), inferior frontal gyrus (IFG), amygdala, insula, and cingulate cortex between ASD and TD individuals. SBM analysis showed alterations in the thickness, volume, and surface area in individuals with ASD in STS, insula, and FFA. Increased cortical thickness was found in individuals with ASD, the IFG. The results of this study provide functional and anatomical bases of social cognition abnormalities in ASD by identifying common signatures from a large pool of neuroimaging studies. These findings provide new insights into the quest for a neuroimaging-based marker for ASD. Hum Brain Mapp 37:3957-3978, 2016. © 2016 Wiley Periodicals, Inc.

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

INTRODUCTION: The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD). METHOD: Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables. RESULTS: Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone. CONCLUSION: The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.

Hemispheric differences in language processing in autism spectrum disorders: A meta-analysis of neuroimaging studies.

Language impairments, a hallmark feature of autism spectrum disorders (ASD), have been related to neuroanatomical and functional abnormalities. Abnormal lateralization of the functional language network, increased reliance on visual processing areas, and increased posterior brain activation have all been reported in ASD and proposed as explanatory models of language difficulties. Nevertheless, inconsistent findings across studies have prevented a comprehensive characterization of the functional language network in ASD. The aim of this study was to quantify common and consistent patterns of brain activation during language processing in ASD and typically developing control (TD) participants using a meta-analytic approach. Activation likelihood estimation (ALE) meta-analysis was used to examine 22 previously published functional Magnetic Resonance Imaging (fMRI)/positron emission tomography studies of language processing (ASD: N = 328; TD: N = 324). Tasks included in this study addressed semantic processing, sentence comprehension, processing figurative language, and speech production. Within-group analysis showed largely overlapping patterns of language-related activation in ASD and TD groups. However, the ASD participants, relative to TD participants, showed: (1) more right hemisphere activity in core language areas (i.e., superior temporal gyrus and inferior frontal gyrus), particularly in tasks where they had poorer performance accuracy; (2) bilateral MTG hypo-activation across many different paradigms; and (3) increased activation of the left lingual gyrus in tasks where they had intact performance. These findings show that the hypotheses reviewed here address the neural and cognitive aspects of language difficulties in ASD across all tasks only in a limited way. Instead, our findings suggest the nuances of language and brain in ASD in terms of its context-dependency. Autism Res 2016, 9: 1046-1057. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders.

Autism spectrum disorders (ASD) are characterized by impairments in social communication and restrictive, repetitive behaviors. While behavioral symptoms are well-documented, investigations into the neurobiological underpinnings of ASD have not resulted in firm biomarkers. Variability in findings across structural neuroimaging studies has contributed to difficulty in reliably characterizing the brain morphology of individuals with ASD. These inconsistencies may also arise from the heterogeneity of ASD, and wider age-range of participants included in MRI studies and in previous meta-analyses. To address this, the current study used coordinate-based anatomical likelihood estimation (ALE) analysis of 21 voxel-based morphometry (VBM) studies examining high-functioning individuals with ASD, resulting in a meta-analysis of 1055 participants (506 ASD, and 549 typically developing individuals). Results consisted of grey, white, and global differences in cortical matter between the groups. Modeled anatomical maps consisting of concentration, thickness, and volume metrics of grey and white matter revealed clusters suggesting age-related decreases in grey and white matter in parietal and inferior temporal regions of the brain in ASD, and age-related increases in grey matter in frontal and anterior-temporal regions. White matter alterations included fiber tracts thought to play key roles in information processing and sensory integration. Many current theories of pathobiology ASD suggest that the brains of individuals with ASD may have less-functional long-range (anterior-to-posterior) connections. Our findings of decreased cortical matter in parietal-temporal and occipital regions, and thickening in frontal cortices in older adults with ASD may entail altered cortical anatomy, and neurodevelopmental adaptations.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates.

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Enhanced parietal cortex activation during location detection in children with autism.

BACKGROUND: Visuospatial processing has been found to be mediated primarily by two cortical routes, one of which is unique to recognizing objects (occipital-temporal, ventral or "what" pathway) and the other to detecting the location of objects in space (parietal-occipital, dorsal or "where" pathway). Considering previous findings of relative advantage in people with autism in visuospatial processing, this functional MRI study examined the connectivity in the dorsal and ventral pathways in high-functioning children with autism. METHODS: Seventeen high-functioning children and adolescents with autism spectrum disorders (ASD) and 19 age-and-IQ-matched typically developing (TD) participants took part in this study. A simple visual task involving object recognition and location detection was used. In the MRI scanner, participants were shown grey scale pictures of objects (e.g., toys, household items, etc.) and were asked to identify the objects presented or to specify the location of objects relative to a cross at the center of the screen. RESULTS: Children with ASD, relative to TD children, displayed significantly greater activation in the left inferior parietal lobule (especially the angular gyrus) while detecting the location of objects. However, there were no group differences in brain activity during object recognition. There were also differences in functional connectivity, with the ASD participants showing decreased connectivity of the inferior temporal area with parietal and occipital areas during location detection. CONCLUSIONS: The results of this study underscore previous findings of an increased reliance on visuospatial processing (increased parietal activation) for information processing in ASD individuals. In addition, such processing may be more local, focal, and detailed in ASD as evidenced from the weaker functional connectivity.

Surface-based morphometry of the cortical architecture of autism spectrum disorders: volume, thickness, area, and gyrification.

Structural neuroimaging studies of autism spectrum disorder (ASD) have uncovered widespread neuroanatomical abnormalities, which may have a significant impact on brain function, connectivity, and on behavioral symptoms of autism. The findings of previous structural MRI studies have largely been distributed across several brain areas, with limited consistency. The current study examined neuroanatomical abnormalities by comparing surface-based measures of cortical morphology (CT: cortical thickness, CSA: cortical surface area, CV: cortical volume, and GI: gyrification index) in 55 high-functioning children and adults with ASD to 60 age-and-IQ-matched typically developing (TD) peers. A few brain areas, the fusiform gyrus (FG), middle temporal gyrus (MTG), and inferior frontal gyrus (IFG), emerged to be primarily different in their morphology between the two groups. Compared to TD participants, ASD participants had significantly smaller CV in left MTG, reduced CSA in bilateral MTG and FG, reduced GI in left supramarginal gyrus, and significantly increased CT in the pars opercularis of the IFG. As a function of age, ASD participants had significant reductions in: CT in the pars opercularis, CSA of the left rostral middle frontal gyrus, and GI for left supramarginal gyrus. Thus, alterations in cortical morphology in ASD were seen primarily in regions that are considered part of the social brain. Overall, these findings point to: neuroanatomical alterations in social brain areas, developmental differences in neuroanatomy, and the need to study neuroanatomy at multiple levels in order to better characterize the cortical architecture of ASD.

G9a/GLP histone lysine dimethyltransferase complex activity in the hippocampus and the entorhinal cortex is required for gene activation and silencing during memory consolidation.

Learning triggers alterations in gene transcription in brain regions such as the hippocampus and the entorhinal cortex (EC) that are necessary for long-term memory (LTM) formation. Here, we identify an essential role for the G9a/G9a-like protein (GLP) lysine dimethyltransferase complex and the histone H3 lysine 9 dimethylation (H3K9me2) marks it catalyzes, in the transcriptional regulation of genes in area CA1 of the rat hippocampus and the EC during memory consolidation. Contextual fear learning increased global levels of H3K9me2 in area CA1 and the EC, with observable changes at the Zif268, DNMT3a, BDNF exon IV, and cFOS gene promoters, which occurred in concert with mRNA expression. Inhibition of G9a/GLP in the EC, but not in the hippocampus, enhanced contextual fear conditioning relative to control animals. The inhibition of G9a/GLP in the EC induced several histone modifications that include not only methylation but also acetylation. Surprisingly, we found that downregulation of G9a/GLP activity in the EC enhanced H3K9me2 in area CA1, resulting in transcriptional silencing of the non-memory permissive gene COMT in the hippocampus. In addition, synaptic plasticity studies at two distinct EC-CA1 cellular pathways revealed that G9a/GLP activity is critical for hippocampus-dependent long-term potentiation initiated in the EC via the perforant pathway, but not the temporoammonic pathway. Together, these data demonstrate that G9a/GLP differentially regulates gene transcription in the hippocampus and the EC during memory consolidation. Furthermore, these findings support the possibility of a role for G9a/GLP in the regulation of cellular and molecular cross talk between these two brain regions during LTM formation.