Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

The significance and limited influence of cerebrovascular reactivity on age and sex effects in task- and resting-state brain activity.

Functional MRI measures the blood-oxygen-level dependent signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state blood-oxygen-level dependent activity and may confound inter-individual effects, such as those related to aging and biological sex. We examined a large dataset containing breath-holding, checkerboard, and resting-state tasks. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and quantified resting-state activity with amplitude of low-frequency fluctuations and regional homogeneity. We hypothesized that cerebrovascular reactivity would be correlated with blood-oxygen-level dependent measures and that accounting for these correlations would result in better estimates of age and sex effects. We found that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with amplitude of low-frequency fluctuations and regional homogeneity in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in amplitude of low-frequency fluctuations and regional homogeneity. However, correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.

Altered white matter functional pathways in Alzheimer's disease.

Alzheimer's disease (AD) is associated with functional disruption in gray matter (GM) and structural damage to white matter (WM), but the relationship to functional signal in WM is unknown. We performed the functional connectivity (FC) and graph theory analysis to investigate abnormalities of WM and GM functional networks and corpus callosum among different stages of AD from a publicly available dataset. Compared to the controls, AD group showed significantly decreased FC between the deep WM functional network (WM-FN) and the splenium of corpus callosum, between the sensorimotor/occipital WM-FN and GM visual network, but increased FC between the deep WM-FN and the GM sensorimotor network. In the clinical groups, the global assortativity, modular interaction between occipital WM-FN and visual network, nodal betweenness centrality, degree centrality, and nodal clustering coefficient in WM- and GM-FNs were reduced. However, modular interaction between deep WM-FN and sensorimotor network, and participation coefficients of deep WM-FN and splenium of corpus callosum were increased. These findings revealed the abnormal integration of functional networks in different stages of AD from a novel WM-FNs perspective. The abnormalities of WM functional pathways connect downward to the corpus callosum and upward to the GM are correlated with AD.

Boosting interpersonal emotion regulation through facial imitation: functional neuroimaging foundations.

Empathic function, which is primarily manifested by facial imitation, is believed to play a pivotal role in interpersonal emotion regulation for mood reinstatement. To explore this association and its neural substrates, we performed a questionnaire survey (study l) to identify the relationship between empathy and interpersonal emotion regulation; and a task-mode fMRI study (study 2) to explore how facial imitation, as a fundamental component of empathic processes, promotes the interpersonal emotion regulation effect. Study 1 showed that affective empathy was positively correlated with interpersonal emotion regulation. Study 2 showed smaller negative emotions in facial imitation interpersonal emotion regulation (subjects imitated experimenter's smile while followed the interpersonal emotion regulation guidance) than in normal interpersonal emotion regulation (subjects followed the interpersonal emotion regulation guidance) and Watch conditions. Mirror neural system (e.g. inferior frontal gyrus and inferior parietal lobe) and empathy network exhibited greater activations in facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation condition. Moreover, facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation exhibited increased functional coupling from mirror neural system to empathic and affective networks during interpersonal emotion regulation. Furthermore, the connectivity of the right orbital inferior frontal gyrus-rolandic operculum lobe mediated the association between the accuracy of facial imitation and the interpersonal emotion regulation effect. These results show that the interpersonal emotion regulation effect can be enhanced by the target's facial imitation through increased functional coupling from mirror neural system to empathic and affective neural networks.

MQGA: A Quantitative Analysis of Brain Network Hubs Using Multi-Graph Theoretical Indices.

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4% sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

The critical mediating roles of the middle temporal gyrus and ventrolateral prefrontal cortex in the dynamic processing of interpersonal emotion regulation.

Interpersonal emotion regulation (IER) is a crucial ability for effectively recovering from negative emotions through social interaction. It has been emphasized that the empathy network, cognitive control network, and affective generation network sustain the deployment of IER. However, the temporal dynamics of functional connectivity among these networks of IER remains unclear. This study utilized IER task-fMRI and sliding window approach to examine both the stationary and dynamic functional connectivity (dFC) of IER. Fifty-five healthy participants were recruited for the present study. Through clustering analysis, four distinct brain states were identified in dFC. State 1 demonstrated situation modification stage of IER, with strong connectivity between affective generation and visual networks. State 2 exhibited pronounced connectivity between empathy network and both cognitive control and affective generation networks, reflecting the empathy stage of IER. Next, a 'top-down' pattern is observed between the connectivity of cognitive control and affective generation networks during the cognitive control stage of state 3. The affective response modulation stage of state 4 mainly involved connections between empathy and affective generation networks. Specifically, the degree centrality of the left middle temporal gyrus (MTG) mediated the association between one's IER tendency and the regulatory effects in state 2. The betweenness centrality of the left ventrolateral prefrontal cortex (VLPFC) mediated the association between one's IER efficiency and the regulatory effects in state 3. Altogether, these findings revealed that dynamic connectivity transitions among empathy, cognitive control, and affective generation networks, with the left VLPFC and MTG playing dominant roles, evident across the IER processing.

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Compressed cerebro-cerebellar functional gradients in children and adolescents with attention-deficit/hyperactivity disorder.

Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.

Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder.

AIM: The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD. METHODS: We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways. RESULTS: Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD. CONCLUSION: The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.

Estimation of neuronal task information in fMRI using zero frequency resonator.

In functional magnetic resonance imaging (fMRI), temporal onsets of BOLD events contain crucial information on activity-inducing signals and make a significant impact in the analysis of functional connectivity (FC). In literature, the estimation of the onsets of the BOLD events from the acquired blood oxygen level-dependent (BOLD) signal using fMRI is mostly performed by choosing locations with a high value of the BOLD signal. This approach may give false onset points because it can incorporate redundant onsets which could be due to non-neuronal activity or can exclude true low-valued BOLD signals. In this study, we present a novel approach to estimating the temporal onsets of the BOLD events using a zero frequency resonator (ZFR) without necessitating information regarding the experimental paradigm (EP). The proposed approach exploits the impulse-like characteristic of activity-inducing signal to estimate the temporal onset points of BOLD events using ZFR which has been widely studied in the area of speech signal processing to estimate the glottal closure instances. The idea behind the approach is that an ideal neuronal impulse has, in principle, equal energy at all frequencies, including around the zero frequency, and will preserve the information of the temporal onsets of the BOLD events at its output. The ZFR-based approach estimates two important features, namely: 1) task-induced temporal onsets of the BOLD events in the fMRI time course and 2) high SNR (HSNR) regions around the estimated BOLD events. Both the estimated features are used to obtain the FC. Results are demonstrated using both the synthetic and experimental (event-related finger tapping and block design working memory) data. We show that a small number of plausible time points, estimated by ZFR, can convey sufficient information indicating the associated activation pattern. The method also illustrates its significance over the conventional correlation and threshold-based conditional rate analysis to estimate FC. The study demonstrates that ZFR-estimated BOLD events and HSNR regions can produce sufficient functionality of the brain in the task paradigm.

The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy.

Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.

Cerebello-cerebral resting-state functional connectivity in spinocerebellar ataxia type 3.

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by progressive motor and nonmotor deficits concomitant with degenerative pathophysiological changes within the cerebellum. The cerebellum is topographically organized into cerebello-cerebral circuits that create distinct functional networks regulating movement, cognition, and affect. SCA3-associated motor and nonmotor symptoms are possibly related not only to intracerebellar changes but also to disruption of the connectivity within these cerebello-cerebral circuits. However, to date, no comprehensive investigation of cerebello-cerebral connectivity in SCA3 has been conducted. The present study aimed to identify cerebello-cerebral functional connectivity alterations and associations with downstream clinical phenotypes and upstream topographic markers of cerebellar neurodegeneration in patients with SCA3. This study included 45 patients with SCA3 and 49 healthy controls. Voxel-based morphometry and resting-state functional magnetic resonance imaging (MRI) were performed to characterize the cerebellar atrophy and to examine the cerebello-cerebral functional connectivity patterns. Structural MRI confirmed widespread gray matter atrophy in the motor and cognitive cerebellum of patients with SCA3. We found reduced functional connectivity between the cerebellum and the cerebral cortical networks, including the somatomotor, frontoparietal, and default networks; however, increased connectivity was observed between the cerebellum and the dorsal attention network. These abnormal patterns correlated with the CAG repeat expansion and deficits in global cognition. Our results indicate the contribution of cerebello-cerebral networks to the motor and cognitive impairments in patients with SCA3 and reveal that such alterations occur in association with cerebellar atrophy. These findings add important insights into our understanding of the role of the cerebellum in SCA3.

White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter.

A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.

A multimodal meta-analysis of regional structural and functional brain alterations in type 2 diabetes.

Neuroimaging studies have identified brain structural and functional alterations of type 2 diabetes mellitus (T2DM) patients; however, there is no systematic information on the relations between abnormalities in these two domains. We conducted a multimodal meta-analysis of voxel-based morphometry and regional resting-state functional MRI studies in T2DM, including fifteen structural datasets (693 patients and 684 controls) and sixteen functional datasets (378 patients and 358 controls). We found, in patients with T2DM compared to controls, conjoint decreased regional gray matter volume (GMV) and altered intrinsic activity mainly in the default mode network including bilateral superior temporal gyrus/Rolandic operculum, left middle and inferior temporal gyrus, and left supramarginal gyrus; decreased GMV alone in the limbic system; and functional abnormalities alone in the cerebellum, insula, and visual cortex. This meta-analysis identified complicated patterns of conjoint and dissociated brain alterations in T2DM patients, which may help provide new insight into the neuropathology of T2DM.

Principal component analysis reveals multiple consistent responses to naturalistic stimuli in children and adults.

Functional MRI (fMRI) study of naturalistic conditions, for example, movie watching, usually focuses on shared responses across subjects. However, individual differences have been attracting increasing attention in search of group differences or associations with behavioral outcomes. Individual differences are typically studied by directly modeling the pair-wise intersubject correlation matrix or projecting the relations onto a single dimension. We contend that it is critical to examine whether there are one or more consistent responses underlying the whole sample, because multiple components, if exist, may undermine the intersubject relations using the previous methods. We propose to use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects and project the individual variability into higher dimensions. By analyzing an fMRI dataset of children and adults watching a cartoon movie, we showed evidence of two consistent responses in the supramarginal gyrus and other regions. While the first components in many regions represented a response pattern mostly in older children and adults, the second components mainly represented the younger children. The second components in the supramarginal network resembled a delayed version of the first PCs for 4 s (2 TR), indicating slower responses in the younger children than the older children and adults. The analyses highlight the importance of identifying multiple consistent responses in responses to naturalistic stimuli. This PCA-based approach could be complementary to the commonly used intersubject correlation to analyze movie-watching data.

Estimations of the weather effects on brain functions using functional MRI: A cautionary note.

The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross-validation using the resting-state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long-term effects using MRI.

The differential association between local neurotransmitter levels and whole-brain resting-state functional connectivity in two distinct cingulate cortex subregions.

We examined the association between rsFC and local neurotransmitter levels in the pregenual anterior cingulate cortex (pgACC) and the anterior mid-cingulate cortex (aMCC) by varying rsFC-strengths at the whole-brain level. Our results showed region-dependent directionality of associations in the investigated ACC subdivisions.

Hypothalamic Atrophy, Expanded CAG Repeat, and Low Body Mass Index in Spinocerebellar Ataxia Type 3.

BACKGROUND: Spinocerebellar ataxia type 3 (SCA3) is an inherited motor disorder that is characterized by low body mass index (BMI). Considering the role of the hypothalamus in regulating appetitive behaviors and metabolism, low BMI may result from hypothalamic degeneration. OBJECTIVES: To examine hypothalamic volume changes in SCA3 by comparing patients and matched healthy controls and to identify potential mediating effects of hypothalamic pathology on CAG repeats for BMI. METHODS: Magnetic resonance imaging datasets of hypothalamic volumes from 41 SCA3 patients and 49 matched controls were analyzed. Relationships among CAG repeat number, hypothalamic volumes, and BMI were assessed using correlation and mediation analyses. RESULTS: SCA3 patients exhibited significant hypothalamic atrophy. Tubular hypothalamic volume was significantly associated with BMI. Mediation analysis revealed an indirect effect of CAG repeat number on BMI via tubular hypothalamic atrophy. CONCLUSIONS: Low BMI in SCA3 is related to neurodegeneration within the tubular hypothalamus, providing a potential target for energy-based treatment. © 2022 International Parkinson and Movement Disorder Society.

Oxytocin Modulates the Intrinsic Dynamics Between Attention-Related Large-Scale Networks.

Attention and salience processing have been linked to the intrinsic between- and within-network dynamics of large-scale networks engaged in internal (default network [DN]) and external attention allocation (dorsal attention network [DAN] and salience network [SN]). The central oxytocin (OXT) system appears ideally organized to modulate widely distributed neural systems and to regulate the switch between internal attention and salient stimuli in the environment. The current randomized placebo (PLC)-controlled between-subject pharmacological resting-state fMRI study in N = 187 (OXT, n = 94; PLC, n = 93; single-dose intranasal administration) healthy male and female participants employed an independent component analysis approach to determine the modulatory effects of OXT on the within- and between-network dynamics of the DAN-SN-DN triple network system. OXT increased the functional integration between subsystems within SN and DN and increased functional segregation of the DN with both attentional control networks (SN and DAN). Whereas no sex differences were observed, OXT effects on the DN-SN interaction were modulated by autistic traits. Together, the findings suggest that OXT may facilitate efficient attention allocation by modulating the intrinsic functional dynamics between DN components and large-scale networks involved in external attentional demands (SN and DAN).

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.