Precentral gyrus functional connectivity signatures of autism

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

CNTN4 modulates neural elongation through interplay with APP

The neuronal cell adhesion molecule contactin-4 (CNTN4) is genetically associated with autism spectrum disorder (ASD) and other psychiatric disorders. Cntn4-deficient mouse models have previously shown that CNTN4 plays important roles in axon guidance and synaptic plasticity in the hippocampus. However, the pathogenesis and functional role of CNTN4 in the cortex has not yet been investigated. Our study found a reduction in cortical thickness in the motor cortex of Cntn4 -/- mice, but cortical cell migration and differentiation were unaffected. Significant morphological changes were observed in neurons in the M1 region of the motor cortex, indicating that CNTN4 is also involved in the morphology and spine density of neurons in the motor cortex. Furthermore, mass spectrometry analysis identified an interaction partner for CNTN4, confirming an interaction between CNTN4 and amyloid-precursor protein (APP). Knockout human cells for CNTN4 and/or APP revealed a relationship between CNTN4 and APP. This study demonstrates that CNTN4 contributes to cortical development and that binding and interplay with APP controls neural elongation. This is an important finding for understanding the physiological function of APP, a key protein for Alzheimer's disease. The binding between CNTN4 and APP, which is involved in neurodevelopment, is essential for healthy nerve outgrowth.

Beyond boundaries: investigating shared and divergent connectivity in the pre-/postcentral gyri and supplementary motor area

OBJECTIVE

This study aimed to comprehensively investigate the functional connectivity of key brain regions involved in motor and sensory functions, namely the precentral gyrus, postcentral gyrus and supplementary motor area (SMA). Using advanced MRI, the objective was to understand the neurophysiological integrative characterizations of these regions by examining their connectivity with eight distinct functional brain networks. The goal was to uncover their roles beyond conventional motor and sensory functions, contributing to a more holistic understanding of brain functioning.

METHODS

The study involved 198 healthy volunteers, with the primary methodology being functional connectivity analysis using advanced MRI techniques. The bilateral precentral gyrus, postcentral gyrus and SMA served as seed regions, and their connectivity with eight distinct brain regional functional networks was investigated. This approach allowed for the exploration of synchronized activity between these critical brain areas, shedding light on their integrated functioning and relationships with other brain networks.

RESULTS

The study revealed a nuanced landscape of functional connectivity for the precentral gyrus, postcentral gyrus and SMA with the main functional brain networks. Despite their high functional connectedness, these regions displayed diverse functional integrations with other networks, particularly in the salience, visual, cerebellar and language networks. Specific data and statistical significance were not provided in the abstract, but the results suggested unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor and sensory functions.

CONCLUSION

The study emphasized the multifaceted roles of the precentral gyrus, postcentral gyrus and SMA. Beyond their crucial involvement in motor and sensory functions, these regions exhibited varied functional integrations with different brain networks. The observed disparities, especially in the salience, visual, cerebellar and language networks, indicated a nuanced and specialized involvement of these regions in diverse cognitive functions. The study underscores the importance of considering the broader neurophysiological landscape to comprehend the intricate roles of these brain areas, contributing to ongoing efforts in unraveling the complexities of brain function.

Influence of transient emotional episodes on affective and cognitive theory of mind

Our emotions may influence how we interact with others. Previous studies have shown an important role of emotion induction in generating empathic reactions towards others' affect. However, it remains unclear whether (and to which extent) our own emotions can influence the ability to infer people's mental states, a process associated with Theory of Mind (ToM) and implicated in the representation of both cognitive (e.g. beliefs and intentions) and affective conditions. We engaged 59 participants in two emotion-induction experiments where they saw joyful, neutral and fearful clips. Subsequently, they were asked to infer other individuals' joy, fear (affective ToM) or beliefs (cognitive ToM) from verbal scenarios. Using functional magnetic resonance imaging, we found that brain activity in the superior temporal gyrus, precuneus and sensorimotor cortices were modulated by the preceding emotional induction, with lower response when the to-be-inferred emotion was incongruent with the one induced in the observer (affective ToM). Instead, we found no effect of emotion induction on the appraisal of people's beliefs (cognitive ToM). These findings are consistent with embodied accounts of affective ToM, whereby our own emotions alter the engagement of key brain regions for social cognition, depending on the compatibility between one's own and others' affect.

Association of polygenic risk for bipolar disorder with resting-state network functional connectivity in youth with and without bipolar disorder

Little is known regarding the polygenic underpinnings of anomalous resting-state functional connectivity (rsFC) in youth bipolar disorder (BD). The current study examined the association of polygenic risk for BD (BD-PRS) with whole-brain rsFC at the large-scale network level in youth with and without BD. 99 youth of European ancestry (56 BD, 43 healthy controls [HC]), ages 13-20 years, completed resting-state fMRI scans. BD-PRS was calculated using summary statistics from the latest adult BD genome-wide association study. Data-driven independent component analyses of the resting-state fMRI data were implemented to examine the association of BD-PRS with rsFC in the overall sample, and separately in BD and HC. In the overall sample, higher BD-PRS was associated with lower rsFC of the salience network and higher rsFC of the frontoparietal network with frontal and parietal regions. Within the BD group, higher BD-PRS was associated with higher rsFC of the default mode network with orbitofrontal cortex, and altered rsFC of the visual network with frontal and occipital regions. Within the HC group, higher BD-PRS was associated with altered rsFC of the frontoparietal network with frontal, temporal and occipital regions. In conclusion, the current study found that BD-PRS generated based on adult genetic data was associated with altered rsFC patterns of brain networks in youth. Our findings support the usefulness of BD-PRS to investigate genetically influenced neuroimaging markers of vulnerability to BD, which can be observed in youth with BD early in their course of illness as well as in healthy youth.

Structural brain network changes in patients with neurofibromatosis type 1: A retrospective study

We investigated the changes in structural connectivity (using diffusion tensor imaging [DTI]) and the structural covariance network based on structural volume using graph theory in patients with neurofibromatosis type 1 (NF1) compared to a healthy control group. We included 14 patients with NF1, according to international consensus recommendations, and 16 healthy individuals formed the control group. This was retrospectively observational study followed STROBE guideline. Both groups underwent brain magnetic resonance imaging including DTI and 3-dimensional T1-weighted imaging. We analyzed structural connectivity using DTI and Diffusion Spectrum Imaging Studio software and evaluated the structural covariance network based on the structural volumes using FreeSurfer and Brain Analysis Using Graph Theory software. There were no differences in the global structural connectivity between the 2 groups, but several brain regions showed significant differences in local structural connectivity. Additionally, there were differences between the global structural covariance networks. The characteristic path length was longer and the small-worldness index was lower in patients with NF1. Furthermore, several regions showed significant differences in the local structural covariance networks. We observed changes in structural connectivity and covariance networks in patients with NF1 compared to a healthy control group. We found that global structural efficiency is decreased in the brains of patients with NF1, and widespread changes in the local structural network were found. These results suggest that NF1 is a brain network disease, and our study provides direction for further research to elucidate the biological processes of NF1.

Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders

Introduction

High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity.

Methods

To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI time series corresponding to the "good friends" in each brain region's friend circle are averaged to obtain a sequence that reflects the intimacy between brain regions in each friend circle. Finally, hypergraph high-order FCN, which reflects the interaction relationships among multiple brain regions, is obtained by calculating the correlations based on the sequence of friend circles.

Results

The experimental results demonstrate that the proposed method outperforms traditional high-order FCN construction methods. Furthermore, integrating the high-order FCN constructed based on hypergraphs and the low-order FCN through feature fusion to achieve complementary information improves the accuracy of assisting in the diagnosis of brain diseases.

Discussion

In addition, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to other brain connectivity patterns.

Case report: An evaluation of early motor skills in an infant later diagnosed with autism

Researchers and clinicians are increasingly interested in understanding the etiology of autism spectrum disorder (ASD) and identifying behaviors that can provide opportunities for earlier detection and therefore earlier onset of intervention activities. One promising avenue of research lies in the early development of motor skills. The present study compares the motor and object exploration behaviors of an infant later diagnosed with ASD (T.I.) with the same skills in a control infant (C.I.). There were notable difference in fine motor skills by just 3 months of age, one of the earliest fine motor differences reported in the literature. In line with previous findings, T.I. and C.I. demonstrated different patterns of visual attention as early as 2.5 months of age. At later visits to the lab, T.I. engaged in unique problem-solving behaviors not demonstrated by the experimenter (i.e., emulation). Overall, findings suggest that infants later diagnosed with ASD may show differences in fine motor skills and visual attention to objects from the first months of life.

Individual brain regulation as learned via neurofeedback is related to affective changes in adolescents with autism spectrum disorder

BACKGROUND

Emotions often play a role in neurofeedback (NF) regulation strategies. However, investigations of the relationship between the induced neuronal changes and improvements in affective domains are scarce in electroencephalography-based studies. Thus, we extended the findings of the first study on slow cortical potential (SCP) NF in autism spectrum disorder (ASD) by linking affective changes to whole-brain activity during rest and regulation.

METHODS

Forty-one male adolescents with ASD were scanned twice at rest using functional magnetic resonance imaging. Between scans, half underwent NF training, whereas the other half received treatment as usual. Furthermore, parents reported on their child's affective characteristics at each measurement. The NF group had to alternatingly produce negative and positive SCP shifts during training and was additionally scanned using functional magnetic resonance imaging while applying their developed regulation strategies.

RESULTS

No significant treatment group-by-time interactions in affective or resting-state measures were found. However, we found increases of resting activity in the anterior cingulate cortex and right inferior temporal gyrus as well as improvements in affective characteristics over both groups. Activation corresponding to SCP differentiation in these regions correlated with the affective improvements. A further correlation was found for Rolandic operculum activation corresponding to positive SCP shifts. There were no significant correlations with the respective achieved SCP regulation during NF training.

CONCLUSION

SCP NF in ASD did not lead to superior improvements in neuronal or affective functioning compared to treatment as usual. However, the affective changes might be related to the individual strategies and their corresponding activation patterns as indicated by significant correlations on the whole-brain level. Trial registration This clinical trial was registered at drks.de (DRKS00012339) on 20th April, 2017.

Neural substrates underlying effortful control deficit in autism spectrum disorder: a meta-analysis of fMRI studies

Effortful control comprises attentional control, inhibitory control, and cognitive flexibility subprocesses. Effortful control is impaired in individuals with autism spectrum disorder, yet its neural underpinnings remain elusive. By conducting a coordinate-based meta-analysis, this study compared the brain activation patterns between autism and typically developing individuals and examined the effect of age on brain activation in each effortful control subprocesses. Meta-analytic results from 22 studies revealed that, individuals with autism showed hypoactivation in the default mode network for tasks tapping inhibitory control functioning (threshold-free cluster enhancement p < 0.001). When these individuals perform tasks tapping attentional control and cognitive flexibility, they exhibited aberrant activation in various brain networks including default mode network, dorsal attention, frontoparietal, visual and somatomotor networks (uncorrected ps < 0.005). Meta-regression analyses revealed that brain regions within the default mode network showed a significant decreasing trend in activation with increasing age (uncorrected p < 0.05). In summary, individuals with autism showed aberrant activation patterns across multiple brain functional networks during all cognitive tasks supporting effortful control, with some regions showing a decrease in activation with increasing age.

Altered functional connectivity between primary motor cortex subregions and the whole brain in patients with incomplete cervical spinal cord injury

To investigate the reorganizations of gray matter volume (GMV) in each subregion of primary motor cortex (M1) after incomplete cervical cord injury (ICCI) and to explore the differences in functional connectivity (FC) between the M1 subregions and the whole brain, and further to disclose the potential value of each M1 subregion in motor function rehabilitation of ICCI patients. Eighteen ICCI patients and eighteen age- and gender- matched healthy controls (HCs) were recruited in this study. The 3D high-resolution T1-weighted structural images and resting-state functional magnetic resonance imaging (rs-fMRI) of all subjects were obtained using a 3.0 Tesla MRI system. Based on the Human Brainnetome Atlas, the structural and functional changes of M1 subregions (including A4hf, A6cdl, A4ul, A4t, A4tl, A6cvl) in ICCI patients were analyzed by voxel-based morphometry (VBM) and seed-based FC, respectively. Compared with HCs, no structural changes in the M1 subregions of ICCI patients was detected. However, when compared with HCs, ICCI patients exhibited decreased FC in visual related areas (lingual gyrus, fusiform gyrus) and sensorimotor related areas (primary sensorimotor cortex) when the seeds were located in bilateral A4hf, A4ul, and decreased FC in visual related areas (lingual gyrus, fusiform gyrus) and cognitive related areas (temporal pole) when the seed was located in the left A4t. Moreover, when the seeds were located in the bilateral A6cdl, decreased FC in visual related areas (lingual gyrus, fusiform gyrus, calcarine gyrus) was also observed. Our findings demonstrated that each of the M1 regions had diverse FC reorganizations, which may provide a theoretical basis for the selection of precise stimulation targets, such as transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tCDS), meanwhile, our results may reveal the possible mechanism of visual feedback and cognitive training to promote motor rehabilitation.

Brain Activity in Age-Related Macular Degeneration Patients From the Perspective of Regional Homogeneity: A Resting-State Functional Magnetic Resonance Imaging Study

Objective

In this study, the regional homogeneity (ReHo) method was used to investigate levels of cerebral homogeneity in individuals with age-related macular degeneration (AMD), with the aim of exploring whether these measures are associated with clinical characteristics.

Materials and Methods

Patients with AMD and healthy controls attending the First Affiliated Hospital of Nanchang University were invited to participate. Resting state functional magnetic resonance images were recorded in each participant and levels of synchronous neural activity were evaluated using ReHo. Receiver operating characteristic (ROC) curves were used to evaluate the sensitivity and specificity of this method.

Results

Eighteen patients with AMD (9 males and 9 females) and 15 healthy controls (HCs) were recruited. The two groups were approximately matched in age, gender and weight. Compared with controls, the ReHo values were significantly higher in the AMD group at the limbic lobe and parahippocampal gyrus, and were significantly reduced at the cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior parietal lobule, and precentral gyrus. Mean ReHo values at the cingulate gyrus and the superior frontal gyrus were negatively correlated with clinical symptoms.

Conclusion

Brain neural homogeneity dysfunction is a manifestation of visual pathways in AMD patients, and may be one of the pathological mechanisms of chronic vision loss, anxiety and depression in AMD patients. In addition, the ReHo data may be useful for early screening for AMD.

Functional cortical associations and their intraclass correlations and heritability as revealed by the fMRI Human Connectome Project

We report on the functional connectivity (FC), its intraclass correlation (ICC), and heritability among 70 areas of the human cerebral cortex. FC was estimated as the Pearson correlation between averaged prewhitened Blood Oxygenation Level-Dependent time series of cortical areas in 988 young adult participants in the Human Connectome Project. Pairs of areas were assigned to three groups, namely homotopic (same area in the two hemispheres), ipsilateral (both areas in the same hemisphere), and heterotopic (nonhomotopic areas in different hemispheres). ICC for each pair of areas was computed for six genetic groups, namely monozygotic (MZ) twins, dizygotic (DZ) twins, singleton siblings of MZ twins (MZsb), singleton siblings of DZ twins (DZsb), non-twin siblings (SB), and unrelated individuals (UNR). With respect to FC, we found the following. (a) Homotopic FC was stronger than ipsilateral and heterotopic FC; (b) average FCs of left and right cortical areas were highly and positively correlated; and (c) FC varied in a systematic fashion along the anterior-posterior and inferior-superior dimensions, such that it increased from anterior to posterior and from inferior to superior. With respect to ICC, we found the following. (a) Homotopic ICC was significantly higher than ipsilateral and heterotopic ICC, but the latter two did not differ significantly from each other; (b) ICC was highest for MZ twins; (c) ICC of DZ twins was significantly lower than that of the MZ twins and higher than that of the three sibling groups (MZsb, DZsb, SB); and (d) ICC was close to zero for UNR. Finally, with respect to heritability, it was highest for homotopic areas, followed by ipsilateral, and heterotopic; however, it did not differ statistically significantly from each other.

Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients

Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

Brain structural alterations in autism and criminal psychopathy

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Autism and psychopathy are both disorders of social cognition and share numerous of their features but still differ distinctively in their clinical phenotype.

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The lower grey matter volumes in the right temporal pole and the left inferior frontal gyrus are the most prominent findings distinguishing violent offenders with high psychopatic from ASD individuals.

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Violent offenders with high psychopatic traits and individuals with ASD both present similar lower grey matter volumes in the right precentral cortex compared to controls.

The goal of this study was to elucidate the anatomical brain basis of social cognition through two disorders with distinctively different phenotypes of social interaction. We compared structural MR images of 20 individuals with autism spectrum disorder (ASD), 19 violent offenders with high psychopathic traits, and 19 control participants using voxel-based morphometry (VBM). Our earlier study showed lower grey matter volume (GMV) values in the insula, frontal cortex, and sensorimotor cortex of the offender group compared to controls. In the present study, the images of the ASD group revealed lower GMV in the left precuneus, right cerebellum, and right precentral gyrus in comparison with controls. The comparison between the offender and ASD groups showed lower GMV values for the right temporal pole and left inferior frontal gyrus in the offender group. There was also an overlap of both disorders in the right pre-central cortex, showing lower GMV compared to controls. Our findings suggest structural differences between violent offenders with high psychopathy traits and ASD individuals in the frontotemporal social brain network areas, previously associated with empathy. We also provide evidence of similar abnormal structures in the motor cortex for both of these disorders, possibly related to uniting issues of social cognition.

Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adult participants (25 ASD and 66 neurotypicals; age: 18-60 years). We examined the relationship between regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging) and autistic traits (using Autism Spectrum Quotient). Our findings show grey matter differences in regions including the orbitofrontal cortex and lingual gyrus, and suggestive evidence for white matter microstructure differences in tracts including the superior longitudinal fasciculus being related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.

A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder

The application of deep learning methods in brain disease diagnosis is becoming a new research hotspot. This study constructed brain functional networks based on the functional magnetic resonance imaging (fMRI) data, and proposed a novel convolutional neural network combined with a prototype learning (CNNPL) framework to classify brain functional networks for the diagnosis of autism spectrum disorder (ASD). At the bottom of CNNPL, traditional CNN was employed as the basic feature extractor, while at the top of CNNPL multiple prototypes were automatically learnt on the features to represent different categories. A generalized prototype loss based on distance cross-entropy was proposed to jointly learn the parameters of the CNN feature extractor and the prototypes. The classification was implemented with prototype matching. A transfer learning strategy was introduced to our CNNPL for weight initialization in the subsequent fine-tuning phase to promote model training. We conducted systematic experiments on the aggregate multi-sites ASD dataset. Experimental results revealed that our model outperforms the current state-of-the-art methods in ASD classification and can reliably learn inter-site biomarkers, indicating the robustness of our model on large-scale dataset with inter-site variability. Furthermore, our model demonstrated robust learning capability for high-level organization of brain functionality. Our study also identified important brain regions as biomarkers associated with ASD classification. Together, our proposed model provides a promising solution for learning and classifying brain functional networks, and thus contributes to the biomarker extraction and imaging diagnosis of ASD.

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI

BACKGROUND

Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.

METHODS

We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.

LIMITATIONS

While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.

RESULTS

Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.

CONCLUSION

This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism.

Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder

While several functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting-state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent. Although there is some evidence for homotopic interhemispheric rsFC alterations in language processing regions in ASD children, wider analyses using large data sets have not been performed. The present study, therefore, conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 ASD and 175 typically developing children under-age 10 and examined associations with symptom severity in the autism brain imaging data exchange data sets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes. Results demonstrated decreased homotopic rsFC in ASD children in the posterior cingulate cortex (PCC) and precuneus of the default mode network, the precentral gyrus of the mirror neuron system, and the caudate of the reward system. Homotopic rsFC of the PCC was associated with symptom severity. Furthermore, although no significant CC volume changes were found in ASD children, there was a significant negative correlation between the anterior CC volumes and homotopic rsFC strengths in the caudate. The present study shows that a reduced pattern of homotopic interhemispheric rsFC in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker. LAY SUMMARY: Homotopic interhemispheric functional connectivity plays an important role in synchronizing activity between the two hemispheres and is altered in adults and adolescents with autism spectrum disorder (ASD). In the present study focused on children with ASD, we have observed a similar pattern of decreased homotopic connectivity, suggesting that alterations in homotopic interhemispheric connectivity may occur early in ASD and be a useful general biomarker across ages.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In this study, we first investigated statistical differences in large-scale resting-state networks (RSNs) on 192 healthy controls (HCs) and 103 ASD patients by using independent component analysis (ICA). Second, an image-based meta-analysis (IBMA) was applied to discover the consistency of spatial patterns from different sites. Last, utilizing these patterns as features, we used Support Vector Machine (SVM) classifier to identify whether a subject was suffering from ASD or not. As a result, six RSNs were obtained with ICA. In each RSN, we identified altered functional connectivity between ASD and HC across the multi-site data. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine the classification performance. The AUC value of classification reaches 0.988. In conclusion, the present study indicates that intrinsic connectivity patterns produced from rsfMRI data could yield a possible biomarker of ASD and contributed to the neurobiology of ASD.

MEG Theta during Lexico-Semantic and Executive Processing Is Altered in High-Functioning Adolescents with Autism

Neuroimaging studies have revealed atypical activation during language and executive tasks in individuals with autism spectrum disorders (ASD). However, the spatiotemporal stages of processing associated with these dysfunctions remain poorly understood. Using an anatomically constrained magnetoencephalography approach, we examined event-related theta oscillations during a double-duty lexical decision task that combined demands on lexico-semantic processing and executive functions. Relative to typically developing peers, high-functioning adolescents with ASD had lower performance accuracy on trials engaging selective semantic retrieval and cognitive control. They showed an early overall theta increase in the left fusiform cortex followed by greater activity in the left-lateralized temporal (starting at ~250 ms) and frontal cortical areas (after ~450 ms) known to contribute to language processing. During response preparation and execution, the ASD group exhibited elevated theta in the anterior cingulate cortex, indicative of greater engagement of cognitive control. Simultaneously increased activity in the ipsilateral motor cortex may reflect a less lateralized and suboptimally organized motor circuitry. Spanning early sensory-specific and late response selection stages, the higher event-related theta responsivity in ASD may indicate compensatory recruitment to offset inefficient lexico-semantic retrieval under cognitively demanding conditions. Together, these findings provide further support for atypical language and executive functions in high-functioning ASD.

Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns

Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks

Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the “black box problem”). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism spectrum disorder (ASD) versus typically developing (TD) controls that has proved difficult to characterize with inferential statistics. To contextualize these findings, we additionally perform classifications of gender and task versus rest. Employing class-balancing to build a training set, we trained [Formula: see text] modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD versus TD, gender, and task versus rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-center dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus.

Alterations of functional connectivities associated with autism spectrum disorder symptom severity: a multi-site study using multivariate pattern analysis

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder. The estimation of ASD severity is very important in clinical practice due to providing a more elaborate diagnosis. Although several studies have revealed some resting-state functional connectivities (RSFCs) that are related to the ASD severity, they have all been based on small-sample data and local RSFCs. The aim of the present study is to adopt multivariate pattern analysis to investigate a subset of connectivities among whole-brain RSFCs that are more contributive to ASD severity estimation based on large-sample data. Regression estimation shows a Pearson correlation value of 0.5 between the estimated and observed severity, with a mean absolute error of 1.41. The results provide obvious evidence that some RSFCs undergo notable alterations with the severity of ASD. More importantly, these selected RSFCs have an abnormality in the connection modes of the inter-network and intra-network connections. In addition, these selected abnormal RSFCs are mainly associated with the sensorimotor network, the default mode network, and inter-hemispheric connectivities, while exhibiting significant left hemisphere lateralization. Overall, this study indicates that some RSFCs suffer from abnormal alterations in patients with ASD, providing additional evidence of large-scale functional network alterations in ASD.

Impaired motor skills and atypical functional connectivity of the sensorimotor system in 40- to 65-year-old adults with autism spectrum disorders

Impairments in fine and gross motor function, coordination, and balance in early development are common in autism spectrum disorders (ASDs). It is unclear whether these deficits persist into adulthood and whether they may be exacerbated by additional motor problems that often emerge in typical aging. We assessed motor skills and used resting-state functional magnetic resonance imaging to study intrinsic functional connectivity of the sensorimotor network in 40- to 65-year-old adults with ASDs (n = 17) and typically developing matched adults (n = 19). Adults with ASDs scored significantly lower on assessments of motor skills compared with an age-matched group of typical control adults. In addition, functional connectivity of the sensorimotor system was reduced and the pattern of connectivity was more heterogeneous in adults with ASDs. A negative correlation between functional connectivity of the motor system and motor skills, however, was only found in the typical control group. Findings suggest behavioral impairment and atypical brain organization of the motor system in middle-age adults with ASDs, accompanied by pronounced heterogeneity.

DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders

There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.

Invertible Network for Classification and Biomarker Selection for ASD

Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify ASD and identify biomarkers for ASD using the connectivity matrix calculated from fMRI as the input. Specifically, with invertible networks, we explicitly determine the decision boundary and the projection of data points onto the boundary. Like linear classifiers, the difference between a point and its projection onto the decision boundary can be viewed as the explanation. We then define the importance as the explanation weighted by the gradient of prediction w.r.t the input, and identify biomarkers based on this importance measure. We perform a regression task to further validate our biomarker selection: compared to using all edges in the connectivity matrix, using the top 10% important edges we generate a lower regression error on 6 different severity scores. Our experiments show that the invertible network is both effective at ASD classification and interpretable, allowing for discovery of reliable biomarkers.

Altered brain cortical maturation is found in adolescents with a family history of alcoholism

Substance-naïve offspring from high-density alcohol use disorder (AUD) families exhibit altered subcortical brain volumes structurally and altered executive-functioning and emotion-processing functionally, compared with their peers. However, there is a dearth of literature exploring alterations of cortical thickness (CTh) in this population. T1-weighted structural brain MRI was acquired in 75 substance-naïve male offspring of treatment-seeking early onset (<25 years) AUD patients with high familial loading of AUDs (≥2 affected relatives) (FHP) and 65 age-matched substance-naïve male controls with negative family history from the community. Surface-based CTh reconstruction was done using FreeSurfer. Univariate general linear models were implemented at each vertex using SurfStat, controlling for age (linear and quadratic effects), and head size, to examine the main effect of familial AUD risk on CTh and its relationship with externalizing symptom score (ESS). A Johnson-Neyman procedure revealed that the main effect of familial AUD risk on CTh was seen during adolescence, where the FHP group had thicker cortices involving bilateral precentral gyri, left caudal middle frontal gyrus (MFG), bilateral temporo-parietal junction, left inferior-frontal gyrus and right inferior-temporal gyrus. Thicker cortices in left MFG and inferior-parietal lobule were also associated with greater ESS within both groups. More importantly, these group differences diminished with age by young adulthood. Familial AUD risk is associated with age-related differences in maturation of several higher order association cortices that are critical to ongoing development in executive function, emotion regulation and social cognition during adolescence. Early supportive intervention for a delay in alcohol initiation during this critical phase may be crucial for this at-risk population.

Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism-an fMRI Perspective

The main objective of this paper is to determine whether resting-state fMRI can identify functional connectivity differences between individuals with autism who experience severe issues with restricted, repetitive, and stereotypical behaviors, those who experience only mild issues, and controls. We use resting-state fMRI data from the ABIDE-I preprocessed repository, with participants grouped according to their ADI-R Restricted, Repetitive, and Stereotyped Patterns of Behavior Subscore. Three processing methods are used for analysis. A time-correlation approach establishes a basic baseline, and we introduce a method based on sliding time windows, with means across time adjusted to consider the fraction of time the correlation measure is above/below average. We complement these with a band-limited coherence approach. For completeness, preprocessing schemes with and without global signal regression are considered. Our results are in line with recent ones which find both over- and under-connectivities in the autistic brain. We find that there are indeed significant differences in connectivity between various regions that differentiate between ASD subjects with severe stereotypical/restrictive behavior issues, those with only mild issues, and controls. Interestingly, for some regions, the "signature" of subjects in the milder of the ASD groups appears to be distinct (i.e., over- or under-connected) relative to both the more severe ASD group and the controls.

Altered resting-state dynamics in autism spectrum disorder: Causal to the social impairment?

Autism spectrum disorder (ASD) is characterized by profound impairments in social abilities and by restricted interests and repetitive behaviors. Much work in the past decade has been dedicated to understanding the brain-bases of ASD, and in the context of resting-state functional connectivity fMRI in high-functioning adolescents and adults, the field has established a set of reliable findings: decreased cortico-cortical interactions among brain regions thought to be engaged in social processing, along with a simultaneous increase in thalamo-cortical and striato-cortical interactions. However, few studies have attempted to manipulate these altered patterns, leading to the question of whether such patterns are actually causally involved in producing the corresponding behavioral impairments. We discuss a few such recent attempts in the domains of fMRI neurofeedback and overt social interaction during scanning, and we conclude that the evidence of causal involvement is somewhat mixed. We highlight the potential role of the thalamus and striatum in ASD and emphasize the need for studies that directly compare scanning during multiple cognitive states in addition to the resting-state.

Overt social interaction and resting state in young adult males with autism: core and contextual neural features

Conversation is an important and ubiquitous social behaviour. Individuals with autism spectrum disorder (autism) without intellectual disability often have normal structural language abilities but deficits in social aspects of communication like pragmatics, prosody, and eye contact. Previous studies of resting state activity suggest that intrinsic connections among neural circuits involved with social processing are disrupted in autism, but to date no neuroimaging study has examined neural activity during the most commonplace yet challenging social task: spontaneous conversation. Here we used functional MRI to scan autistic males (n = 19) without intellectual disability and age- and IQ-matched typically developing control subjects (n = 20) while they engaged in a total of 193 face-to-face interactions. Participants completed two kinds of tasks: conversation, which had high social demand, and repetition, which had low social demand. Autistic individuals showed abnormally increased task-driven interregional temporal correlation relative to controls, especially among social processing regions and during high social demand. Furthermore, these increased correlations were associated with parent ratings of participants' social impairments. These results were then compared with previously-acquired resting state data (56 autism, 62 control subjects). While some interregional correlation levels varied by task or rest context, others were strikingly similar across both task and rest, namely increased correlation among the thalamus, dorsal and ventral striatum, somatomotor, temporal and prefrontal cortex in the autistic individuals, relative to the control groups. These results suggest a basic distinction. Autistic cortico-cortical interactions vary by context, tending to increase relative to controls during task and decrease during test. In contrast, striato- and thalamocortical relationships with socially engaged brain regions are increased in both task and rest, and may be core to the condition of autism.

Association between Thalamocortical Functional Connectivity Abnormalities and Cognitive Deficits in Schizophrenia

Cognitive deficits are considered a core component of schizophrenia and may predict functional outcome. However, the neural underpinnings of neuropsychological impairment remain to be fully elucidated. Data of 59 schizophrenia patients and 72 healthy controls from a public resting-state fMRI database was employed in our study. Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery was used to measure deficits of cognitive abilities in schizophrenia. Neural correlates of cognitive deficits in schizophrenia were examined by linear regression analysis of the thalamocortical network activity with scores of seven cognitive domains. We confirmed the combination of reduced prefrontal-thalamic connectivity and increased sensorimotor-thalamic connectivity in patients with schizophrenia. Correlation analysis with cognition revealed that in schizophrenia (1) the thalamic functional connectivity in the bilateral pre- and postcentral gyri was negatively correlated with attention/vigilance and speed of processing (Pearson’s r ≤ −0.443, p ≤ 0.042, FWE corrected), and positively correlated with patients’ negative symptoms (Pearson’s r ≥ 0.375, p ≤ 0.003, FWE corrected); (2) the thalamic functional connectivity in the right cerebellum was positively correlated with speed of processing (Pearson’s r = 0.388, p = 0.01, FWE corrected). Our study demonstrates that thalamic hyperconnectivity with sensorimotor areas is related to the severity of cognitive deficits and clinical symptoms, and extends our understanding of the neural underpinnings of “cognitive dysmetria” in schizophrenia.

Distinct Methylphenidate-Evoked Response Measured Using Functional Near-Infrared Spectroscopy During Go/No-Go Task as a Supporting Differential Diagnostic Tool Between Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Comorbid Children

Attention deficit/hyperactivity disorder (ADHD) has been frequently reported as co-occurring with autism spectrum disorder (ASD). However, ASD-comorbid ADHD is difficult to diagnose since clinically significant symptoms are similar in both disorders. Therefore, we propose a classification method of differentially recognizing the ASD-comorbid condition in ADHD children. The classification method was investigated based on functional brain imaging measured by near-infrared spectroscopy (NIRS) during a go/no-go task. Optimization and cross-validation of the classification method was carried out in medicated-naïve and methylphenidate (MPH) administered ADHD and ASD-comorbid ADHD children (randomized, double-blind, placebo-controlled, and crossover design) to select robust parameters and cut-off thresholds. The parameters could be defined as either single or averaged multi-channel task-evoked activations under an administration condition (i.e., pre-medication, post-MPH, and post-placebo). The ADHD children were distinguished by significantly high MPH-evoked activation in the right hemisphere near the midline vertex. The ASD-comorbid ADHD children tended to have low activation responses in all regions. High specificity (86 ± 4.1%; mean ± SD), sensitivity (93 ± 7.3%), and accuracy (82 ± 1.6%) were obtained using the activation of oxygenated-hemoglobin concentration change in right middle frontal, angular, and precentral gyri under MPH medication. Therefore, the significantly differing MPH-evoked responses are potentially effective features and as supporting differential diagnostic tools.

Characterization of Autism Spectrum Disorder across the Age Span by Intrinsic Network Patterns

Autism spectrum disorder (ASD) is characterized by abnormal functional organization of brain networks, which may underlie the cognitive and social impairments observed in affected individuals. The present study characterizes unique intrinsic connectivity within- and between- neural networks in children through to adults with ASD, relative to controls. Resting state fMRI data were analyzed in 204 subjects, 102 with ASD and 102 age- and sex-matched controls (ages 7-40 years), acquired on a single scanner. ASD was assessed using the autism diagnostic observation schedule (ADOS). BOLD correlations were calculated between 47 regions of interest, spanning seven resting state brain networks. Partial least squares (PLS) analyses evaluated the association between connectivity patterns and ASD diagnosis as well as ASD severity scores. PLS demonstrated dissociable connectivity patterns in those with ASD, relative to controls. Similar patterns were observed in the whole cohort and in a subgroup analysis of subjects under 18 years of age. Greater inter-network connectivity was seen in ASD with greater intra-network connectivity in controls. In conclusion, stronger inter-network and weaker intra-network resting state-fMRI BOLD correlations characterize ASD and may differentiate control and ASD cohorts. These findings are relevant to understanding ASD as a disruption of network topology.

Relationship between Autistic Spectrum Trait and Regional Cerebral Blood Flow in Healthy Male Subjects

OBJECTIVE

Autistic spectrum traits are postulated to lie on a continuum that extends between individuals with autism and individuals with typical development. The present study was carried out to investigate functional and network abnormalities associated with autistic spectrum trait in healthy male subjects.

METHODS

Subjects were 41 healthy male subjects who underwent the social responsiveness scale-adult (SRS-A) and magnetic resonance imaging.

RESULTS

There was significant positive correlation between the total score of SRS-A and the regional cerebral blood flow (CBF) in posterior cingulate cortex (PCC). Also, there were changes in functional network such as in cingulate corti, insula and fusiform cortex. Further, we also found the significant difference of functional networks between the healthy male subjects with high or low autistic spectrum trait, and these points were congruent with the previous perceptions derived from autistic-spectrum disorders.

CONCLUSION

These findings suggest a biological basis for the autistic spectrum trait and may be useful for the imaging marker of autism symptomatology.

Dynamic functional connectivity analysis reveals decreased variability of the default-mode network in developing autistic brain

Accumulating neuroimaging evidence suggests that abnormal functional connectivity of the default mode network (DMN) contributes to the social-cognitive deficits of autism spectrum disorder (ASD). Although most previous studies relied on conventional functional connectivity methods, which assume that connectivity patterns remain constant over time, understanding the temporal dynamics of functional connectivity during rest may provide new insights into the dysfunction of the DMN in ASD. In this work, dynamic functional connectivity analysis based on sliding time window correlation was applied to the resting-state functional magnetic resonance imaging data of 28 young children with ASD (age range: 3-7 years) and 29 matched typically developing controls (TD group). In addition, k-means cluster analysis was performed to identify distinct temporal states based on the spatial similarity of each functional connectivity pattern. Compared with the TD group, young children with ASD showed decreased dynamic functional connectivity variance between the posterior cingulate cortex (PCC) and the right precentral gyrus, which is negatively correlated with social motivation and social relating. Cluster analysis revealed significant differences in functional connectivity patterns between the ASD and TD groups in discrete temporal states. Our findings reveal that atypical dynamic interactions between the PCC and sensorimotor cortex are associated with social deficits in ASD. Results also highlight the critical role of PCC in the social-cognitive deficits of ASD and support the concept that understanding the dynamic neural interactions among brain regions can provide insights into functional abnormalities in ASD. Autism Research 2018, 11: 1479-1493. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Social cognitive dysfunction in autism spectrum disorder (ASD) is associated with dysfunction of the default mode network (DMN), a set of brain areas involved in various domains of social processing. We found that decreases in the dynamic functional connectivity variance between the posterior cingulate cortex and the sensorimotor cortex are associated with deficits in social motivation and social relating in young children with ASD. This result suggests that aberrations in the DMN and its dynamic interactions with other networks contribute to atypical integration of information with respect to self and others.

Atypical auditory language processing in adolescents with autism spectrum disorder

OBJECTIVE

Individuals with autism spectrum disorder (ASD) often show characteristic differences in auditory processing. To clarify the mechanisms underlying communication impairment in ASD, we examined auditory language processing with both anatomical and functional methods.

METHODS

We assessed the language abilities of adolescents with ASD and typically developing (TD) adolescents, and analyzed the surface-based morphometric structure between the groups using magnetic resonance imaging. Furthermore, we measured cortical responses to an auditory word comprehension task with magnetoencephalography and performed network-based statistics using the phase locking values.

RESULTS

We observed no structural differences between the groups. However, the volume of the left ventral central sulcus (vCS) showed a significant correlation with linguistic scores in ASD. Moreover, adolescents with ASD showed weaker cortical activation in the left vCS and superior temporal sulcus. Furthermore, these regions showed differential correlations with linguistic scores between the groups. Moreover, the ASD group had an atypical gamma band (25-40 Hz) network centered on the left vCS.

CONCLUSIONS

Adolescents with ASD showed atypical responses on the auditory word comprehension task and functional brain differences.

SIGNIFICANCE

Our results suggest that phonological processing and gamma band cortical activity play a critical role in auditory language processing-related pathophysiology in adolescents with ASD.

Abnormal Functional Connectivity of Resting State Network Detection Based on Linear ICA Analysis in Autism Spectrum Disorder

Some functional magnetic resonance imaging (fMRI) researches in autism spectrum disorder (ASD) patients have shown that ASD patients have significant impairment in brain response. However, few researchers have studied the functional structure changes of the eight resting state networks (RSNs) in ASD patients. Therefore, research on statistical differences of RSNs between 42 healthy controls (HC) and 50 ASD patients has been studied using linear independent component analysis (ICA) in this paper. Our researches showed that there was abnormal functional connectivity (FC) of RSNs in ASD patients. The RSNs with the decreased FC and increased FC in ASD patients included default mode network (DMN), central executive network (CEN), core network (CN), visual network (VN), self-referential network (SRN) compared to HC. The RSNs with the increased FC in ASD patients included auditory network (AN), somato-motor network (SMN). The dorsal attention network (DAN) in ASD patients showed the decreased FC. Our findings indicate that the abnormal FC in RSNs extensively exists in ASD patients. Our results have important contribution for the study of neuro-pathophysiological mechanisms in ASD patients.

Complex Network Measures in Autism Spectrum Disorders

Recent studies have suggested abnormal brain network organization in subjects with Autism Spectrum Disorders (ASD). Here we applied spectral clustering algorithm, diverse centrality measures (betweenness (BC), clustering (CC), eigenvector (EC), and degree (DC)), and also the network entropy (NE) to identify brain sub-systems associated with ASD. We have found that BC increases in the following ASD clusters: in the somatomotor, default-mode, cerebellar, and fronto-parietal. On the other hand, CC, EC, and DC decrease in the somatomotor, default-mode, and cerebellar clusters. Additionally, NE decreases in ASD in the cerebellar cluster. These findings reinforce the hypothesis of under-connectivity in ASD and suggest that the difference in the network organization is more prominent in the cerebellar system. The cerebellar cluster presents reduced NE in ASD, which relates to a more regular organization of the networks. These results might be important to improve current understanding about the etiological processes and the development of potential tools supporting diagnosis and therapeutic interventions.

Differential Fairness Decisions and Brain Responses After Expressed Emotions of Others in Boys with Autism Spectrum Disorders

Little is known about how emotions expressed by others influence social decisions and associated brain responses in autism spectrum disorders (ASD). We investigated the neural mechanisms underlying fairness decisions in response to explicitly expressed emotions of others in boys with ASD and typically developing (TD) boys. Participants with ASD adjusted their allocation behavior in response to the emotions but reacted less unfair than TD controls in response to happiness. We also found reduced brain responses in the precental gyrus in the ASD versus TD group when receiving happy versus angry reactions and autistic traits were positively associated with activity in the postcentral gyrus. These results provide indications for a role of precentral and postcentral gyrus in social-affective difficulties in ASD.

Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers

Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain-behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction.

Action simulation and mirroring in children with autism spectrum disorders

Mental imitation, perhaps a precursor to motor imitation, involves visual perspective-taking and motor imagery. Research on mental imitation in autism spectrum disorders (ASD) has been rather limited compared to that on motor imitation. The main objective of this fMRI study is to determine the differences in brain responses underlying mirroring and mentalizing networks during mental imitation in children and adolescents with ASD. Thirteen high-functioning children and adolescents with ASD and 15 age-and- IQ-matched typically developing (TD) control participants took part in this fMRI study. In the MRI scanner, participants were shown cartoon pictures of people performing everyday actions (Transitive actions: e.g., ironing clothes but with the hand missing; and Intransitive actions: e.g., clapping hands with the palms missing) and were asked to identify which hand or palm orientation would best fit the gap. The main findings are: 1) both groups performed equally while processing transitive and intransitive actions; 2) both tasks yielded activation in the bilateral inferior frontal gyrus (IFG) and inferior parietal lobule (IPL) in ASD and TD groups; 3) Increased activation was seen in ASD children, relative to TD, in left ventral premotor and right middle temporal gyrus during intransitive actions; and 4) ASD symptom severity positively correlated with activation in left parietal, right middle temporal, and right premotor regions across all subjects. Overall, our findings suggest that regions mediating mirroring may be recruiting more brain resources in ASD and may have implications for understanding social movement through modeling.

Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data

Significance: Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.