Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification.

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals.

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar cross-disorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Binding of Dopamine D1 Receptor and Noradrenaline Transporter in Individuals with Autism Spectrum Disorder: A PET Study.

Although previous studies have suggested the involvement of dopamine (DA) and noradrenaline (NA) neurotransmissions in the autism spectrum disorder (ASD) pathophysiology, few studies have examined these neurotransmissions in individuals with ASD in vivo. Here, we investigated DA D1 receptor (D1R) and noradrenaline transporter (NAT) binding in adults with ASD (n = 18) and neurotypical controls (n = 20) by utilizing two different PET radioligands, [11C]SCH23390 and (S,S)-[18F]FMeNER-D2, respectively. We found no significant group differences in DA D1R (striatum, anterior cingulate cortex, and temporal cortex) or NAT (thalamus and pons) binding. However, in the ASD group, there were significant negative correlations between DA D1R binding (striatum, anterior cingulate cortex and temporal cortex) and the "attention to detail" subscale score of the Autism Spectrum Quotient. Further, there was a significant positive correlation between DA D1R binding (temporal cortex) and emotion perception ability assessed by the neurocognitive battery. Associations of NAT binding with empathic abilities and executive function were found in controls, but were absent in the ASD group. Although a lack of significant group differences in binding might be partly due to the heterogeneity of ASD, our results indicate that central DA and NA function might play certain roles in the clinical characteristics of ASD.

Functional connectomes linking child-parent relationships with psychological problems in adolescence.

The child-parent relationship is a significant factor in an adolescent's well-being and functional outcomes. Epidemiological evidence indicates that relationships with the father and mother are differentially associated with specific psychobehavioral problems that manifest differentially between boys and girls. Neuroimaging is expected to bridge the gap in understanding such a complicated mapping between the child-parent relationships and adolescents' problems. However, possible differences in the effects of child-father and child-mother relationships on sexual dimorphism in children's brains and psychobehavioral problems have not been examined yet. This study used a dataset of 10- to 13-year-old children (N â€‹= â€‹93) to reveal the triad of associations among child-parent relationship, brain, and psychobehavioral problems by separately estimating the respective effects of child-father and child-mother relationships on boys and girls. We first fitted general linear models to identify the effects of paternal and maternal relationships in largely different sets of children's resting-state functional connectivity, which we term paternal and maternal functional brain connectomes (FBCs). We then performed connectome-based predictive modeling (CPM) to predict children's externalizing and internalizing problems from these parental FBCs. The models significantly predicted a range of girls' internalizing problems, whereas the prediction of boys' aggression was also significant using a more liberal uncorrected threshold. A series of control analyses confirmed that CPMs using FBCs associated with peer relationship or family socioeconomic status failed to make significant predictions of psychobehavioral problems. Lastly, a causal discovery method identified causal paths from daughter-mother relationship to maternal FBC, and then to daughter's internalizing problems. These observations indicate sex-dependent mechanisms linking child-parent relationship, brain, and psychobehavioral problems in the development of early adolescence.

Role of the right temporoparietal junction in intergroup bias in trust decisions.

Intergroup bias, which is the tendency to behave more positively toward an in-group member than toward an out-group member, is pervasive in real life. In particular, intergroup bias in trust decisions substantially influences multiple areas of life and thus better understanding of this tendency can provide significant insights into human social behavior. Although previous functional magnetic resonance imaging studies showed the involvement of the right temporoparietal junction (TPJ) in intergroup trust bias, a causal relationship between the two has rarely been explored. By combining repetitive transcranial magnetic stimulation and a newly developed trust game task, we investigated the causal role of the right TPJ in intergroup bias in trust decisions. In the trust game task, the counterpart's group membership (in-group or out-group) and reciprocity were manipulated. We applied either neuronavigated inhibitory continuous theta burst stimulation (cTBS) or sham stimulation over the right TPJ before performing the trust game task in healthy volunteers. After the sham stimulation, the participants' degrees of investments with in-group members were significantly higher than those with out-group members. However, after cTBS to the right TPJ, this difference was not observed. The current results extend previous findings by showing that the causal roles of the right TPJ can be observed in intergroup bias in trust decisions. Our findings add to our understanding of the mechanisms of human social behavior.

Impact of past experiences on decision-making in autism spectrum disorder.

People are often influenced by past costs in their current decision-making, thus succumbing to a well-known bias recognized as the sunk cost effect. A recent study showed that the sunk cost effect is attenuated in individuals with autism spectrum disorder (ASD). However, the study only addressed one situation of utilization decision by focusing on the choice between similar attractive alternatives with different levels of sunk costs. Thus, it remains unclear how individuals with ASD behave under sunk costs in different types of decision situations, particularly progress decisions, in which the decision-maker allocates additional resources to an initially chosen alternative. The sunk cost effect in progress decisions was estimated using an economic task designed to assess the effect of the past investments on current decision-making. Twenty-four individuals with ASD and 21 age-, sex-, smoking status-, education-, and intelligence quotient-level-matched typical development (TD) subjects were evaluated. The TD participants were more willing to make the second incremental investment if a previous investment was made, indicating that their decisions were influenced by sunk costs. However, unlike the TD group, the rates of investments were not significantly increased after prior investments in the ASD group. The results agree with the previous evidence of a reduced sensitivity to context stimuli in individuals with ASD and help us obtain a broader picture of the impact of sunk costs on their decision-making. Our findings will contribute to a better understanding of ASD and may be useful in addressing practical implications of their socioeconomic behavior.

Online cathodal transcranial direct current stimulation to the right homologue of Broca's area improves speech fluency in people who stutter.

AIM: Previous functional imaging studies demonstrate that people who stutter (PWS) exhibit over- and under-activation of Broca's and Wernicke's areas and their right hemisphere homologues when speaking. However, it is unclear whether this altered activation represents the neural cause of speech dysfluency or a secondary compensatory activation in PWS. To clarify the functional significance of the altered activation pattern in classic language areas and their right homologues, we examined whether the severity of stuttering was affected when the activation of these areas was modulated by brain stimulation. METHODS: While PWS read passages aloud, we applied transcranial direct current stimulation (tDCS) using electrode montages that included an anodal or cathodal electrode placed over one of the language areas and its right hemisphere homologue, with the second electrode placed over the contralateral supraorbital region. Each participant underwent both anodal and cathodal tDCS sessions, each of which included a sham stimulation. Effects of stimulation polarity and electrode location on the frequency of stuttering were analyzed. RESULTS: We observed a significant interaction between polarity and location on the frequency of stuttering. Follow-up analyses revealed that a tDCS montage including the cathodal electrode over right Broca's area (RB) significantly reduced the frequency of stuttering. CONCLUSION: The results indicated that stuttering severity was ameliorated when overactivation in RB was reduced by tDCS. This observation further suggests that speech dysfluency in PWS may be caused either by functional alteration in RB or by abnormal activation in speech motor control areas that are connected with RB.

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder.

AIM: Prior structural magnetic resonance imaging studies demonstrated atypical gray matter characteristics in siblings of individuals with autism spectrum disorder (ASD). However, they did not clarify which aspect of gray matter is related to the endophenotype (i.e., genetic vulnerability) of ASD. Further, because they did not enroll siblings of typically developing (TD) people, they may have underestimated the difference between individuals with ASD and their unaffected siblings. The current study aimed to address these gaps. METHODS: We recruited 30 pairs of adult male siblings (15 pairs with an ASD endophenotype and 15 pairs without) and focused on four gray matter parameters: cortical volume and three surface-based parameters (cortical thickness, fractal dimension, and sulcal depth [SD]). First, we sought to identify a pattern of an ASD endophenotype, comparing the four parameters. Then, we compared individuals with ASD and their unaffected siblings in the cortical parameters to identify neural correlates for the clinical diagnosis accounting for the difference between TD siblings. RESULTS: A sparse logistic regression with a leave-one-pair-out cross-validation showed the SD as having the highest accuracy for the identification of an ASD endophenotype (73.3%) compared with the other three parameters. A bootstrapping analysis accounting for the difference in the SD between TD siblings showed a significantly large difference between individuals with ASD and their unaffected siblings in six out of 68 regions of interest. CONCLUSION: This proof-of-concept study suggests that an ASD endophenotype emerges in the SD and that neural bases for ASD diagnosis can be discerned from the endophenotype when accounting for the difference between TD siblings.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers.

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

Development of a neurofeedback protocol targeting the frontal pole using near-infrared spectroscopy.

AIM: Neurofeedback has been studied with the aim of controlling cerebral activity. Near-infrared spectroscopy is a non-invasive neuroimaging technique used for measuring hemoglobin concentration changes in cortical surface areas with high temporal resolution. Thus, near-infrared spectroscopy may be useful for neurofeedback, which requires real-time feedback of repeated brain activation measurements. However, no study has specifically targeted neurofeedback, using near-infrared spectroscopy, in the frontal pole cortex. METHODS: We developed an original near-infrared spectroscopy neurofeedback system targeting the frontal pole cortex. Over a single day of testing, each healthy participant (n = 24) received either correct or incorrect (Sham) feedback from near-infrared spectroscopy signals, based on a crossover design. RESULTS: Under correct feedback conditions, significant activation was observed in the frontal pole cortex (P = 0.000073). Additionally, self-evaluation of control and metacognitive beliefs were associated with near-infrared spectroscopy signals (P = 0.006). CONCLUSION: The neurofeedback system developed in this study might be useful for developing control of frontal pole cortex activation.

Meta-analysis of structural integrity of white matter and functional connectivity in developmental stuttering.

Developmental stuttering is a speech disfluency disorder characterized by repetitions, prolongations, and blocks of speech. While a number of neuroimaging studies have identified alterations in localized brain activation during speaking in persons with stuttering (PWS), it is unclear whether neuroimaging evidence converges on alterations in structural integrity of white matter and functional connectivity (FC) among multiple regions involved in supporting fluent speech. In the present study, we conducted coordinate-based meta-analyses according to the PRISMA guidelines for available publications that studied fractional anisotropy (FA) using tract-based spatial statistics (TBSS) for structural integrity and the seed-based voxel-wise FC analyses. The search retrieved 11 publications for the TBSS FA studies, 29 seed-based FC datasets from 6 publications for the resting-state, and 29 datasets from 6 publications for the task-based studies. The meta-analysis of TBSS FA revealed that PWS exhibited FA reductions in the middle and posterior segments of the left superior longitudinal fasciculus. Furthermore, the analysis of resting-state FC demonstrated that PWS had reduced FC in the right supplementary motor area and inferior parietal cortex, whereas an increase in FC was observed in the left cerebellum crus I. Conversely, we observed increased FC for task-based FC in regions implicated in speech production or sequential movements, including the anterior cingulate cortex, posterior insula, and bilateral cerebellum crus I in PWS. Functional network characterization of the altered FCs revealed that the sets of reduced resting-state and increased task-based FCs were largely distinct, but the somatomotor and striatum/thalamus networks were foci of alterations in both conditions. These observations indicate that developmental stuttering is characterized by structural and functional alterations in multiple brain networks that support speech fluency or sequential motor processes, including cortico-cortical and subcortical connections.

Neural correlates of perceptual switching and their association with empathy and alexithymia in individuals with and without autism spectrum disorder.

Individuals with autism spectrum disorder (ASD) often show limited empathy (poor recognition of others' emotions) and high alexithymia (poor recognition of own emotions and external thinking), which can negatively impact their social functioning. Previous experimental studies suggest that alterations in cognitive flexibility play key roles in the development of these characteristics in ASD. However, the underlying neural mechanisms that link cognitive flexibility and empathy/alexithymia are still largely unknown. In this study, we examined the neural correlates of cognitive flexibility via functional magnetic resonance imaging during perceptual task-switching in typical development (TD) adults and adults with ASD. We also investigated associations between regional neural activity and psychometric empathy and alexithymia scores among these populations. In the TD group, stronger activation of the left middle frontal gyrus was associated with better perceptual switching and greater empathic concern. Among individuals with ASD, stronger activation of the left inferior frontal gyrus was associated with better perceptual switching, greater empathy, and lower alexithymia. These findings will contribute to develop a better understanding of social cognition, and could be informative for the development of new ASD therapies.

Increased glutamate and glutamine levels and their relationship to astrocytes and dopaminergic transmissions in the brains of adults with autism.

Increased excitatory neuronal tones have been implicated in autism, but its mechanism remains elusive. The amplified glutamate signals may arise from enhanced glutamatergic circuits, which can be affected by astrocyte activation and suppressive signaling of dopamine neurotransmission. We tested this hypothesis using magnetic resonance spectroscopy and positron emission tomography scan with 11C-SCH23390 for dopamine D1 receptors in the anterior cingulate cortex (ACC). We enrolled 18 male adults with high-functioning autism and 20 typically developed (TD) male subjects. The autism group showed elevated glutamate, glutamine, and myo-inositol (mI) levels compared with the TD group (p = 0.045, p = 0.044, p = 0.030, respectively) and a positive correlation between glutamine and mI levels in the ACC (r = 0.54, p = 0.020). In autism and TD groups, ACC D1 receptor radioligand binding was negatively correlated with ACC glutamine levels (r = - 0.55, p = 0.022; r = - 0.58, p = 0.008, respectively). The enhanced glutamate-glutamine metabolism might be due to astroglial activation and the consequent reinforcement of glutamine synthesis in autistic brains. Glutamine synthesis could underly the physiological inhibitory control of dopaminergic D1 receptor signals. Our findings suggest a high neuron excitation-inhibition ratio with astrocytic activation in the etiology of autism.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study.

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P<0.05, family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study.

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites and different developmental stages. Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults and Japanese adults. The neuromarker demonstrated significant generalization for children and adolescents. We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

An fMRI study of reduced perceptual load-dependent modulation of task-irrelevant activity in adults with autism spectrum conditions.

Recent studies on selective attention have demonstrated that the perceptual load of a task determines the processing stage at which irrelevant sensory stimuli are filtered out. Although individuals with autism spectrum conditions (ASC) have been repeatedly reported to display several kinds of abnormal behavior related to attention deficits, the neural mechanisms underlying these deficits have not been well investigated within the framework of the load dependency of selective attention. The present study used functional magnetic resonance imaging (fMRI) to examine the brain responses of adults with high-functioning ASC to irrelevant visual distractors while performing a visual target detection task under high or low perceptual load. We observed that the increased perceptual load activated regions of the fronto-parietal attention network of controls and ASC comparably. On the other hand, the visual cortex activity evoked by visual distractors was less modulated by the increased perceptual load in ASC than in controls. Simple regression analyses showed that the degree of the modulation was significantly correlated with the severity of the autistic symptoms. We also observed reduced load-dependent modulation of the functional connectivity between the intraparietal and visual regions in the ASC group. These results revealed neural correlates for abnormal perceptual load-dependent engagement of visual attention in ASC, which may underlie aspects of cognitive and behavioral characteristics of these disorders.