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/GLP lysine dimethyltransferase complex and the histone H3 lysine 9 di-methylation (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 down-regulation 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 role for G9a/GLP in the regulation of cellular and molecular cross-talk between these two brain regions during LTM formation.

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. In order 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 (pSTG), middle frontal gyrus (MFG), 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.

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.

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.

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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.

IntroductionThe 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).MethodVerbal 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.ResultsClassifiers 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.ConclusionThe 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.

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.