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Altered white matter microstructure has been reported repeatedly using diffusion tensor imaging (DTI) in HIV-associated neurocognitive disorders. However, the associations between neurocognitive deficits and impaired white matter remains obscure due to frequent physical and psychiatric comorbidities in the patients. Severe immune suppression, reflected by low nadir CD4 T-cell counts, is reported to be associated with the neurocognitive deficits in the patients. In the present study, we examined white matter integrity using DTI and tract-based spatial statistics (TBSS), and neurocognitive functions using a battery of tests, in 15 HIV-infected patients with low nadir CD4, 16 HIV-infected patients with high nadir CD4, and 33 age- and sex-matched healthy controls. As DTI measures, we analyzed fractional anisotropy (FA) and mean diffusivity (MD). In addition, we investigated the correlation between white matter impairments and neurocognitive deficits. Among the three participant groups, the patients with low nadir CD4 showed significantly lower performance in processing speed and motor skills, and had significantly increased MD in widespread regions of white matter in both hemispheres. In the patients with low nadir CD4, there was a significant negative correlation between motor skills and MD in the right motor tracts, as well as in the corpus callosum. In summary, this study may provide white matter correlates of neurocognitive deficits in HIV-infected patients with past severe immune suppression as legacy effects.

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; allP

Background and HypothesisDynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.Study DesignWe applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.Study ResultsDCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.ConclusionsDCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Although neuropsychological studies of human immunodeficiency virus (HIV)-infected patients have demonstrated heterogeneity in neurocognitive impairment and neuroimaging studies have reported diverse brain regions affected by HIV, it remains unclear whether individual differences in neurocognitive impairment are underpinned by their neural bases. Here, we investigated spatial distribution patterns of correlation between neurocognitive function and regional gray matter (GM) volume across patients with HIV. Thirty-one combination antiretroviral therapy-treated HIV-infected Japanese male patients and 33 age- and sex-matched healthy controls were included in the analysis after strict exclusion criteria, especially for substance use. Fifteen neurocognitive tests were used, and volumetric magnetic resonance imaging was performed. We used voxel-based morphometry to compare GM volume between groups and identify regional GM volumes that correlated with neurocognitive tests across patients. Using the Frascati criteria, 10 patients were diagnosed with asymptomatic neurocognitive impairment, while the others were not diagnosed with HIV-associated neurocognitive disorders. Patients showed a significantly lower performance in five neurocognitive tests as well as significantly reduced GM volume relative to controls, with volume-reduced regions spread diffusely across the whole brain. Different aspects of neurocognitive impairment (i.e., figural copy, finger tapping, and Pegboard) were associated with different GM regions. Our findings suggest a biological background constituting heterogeneity of neurocognitive impairment in HIV infection and support the clinical importance of considering individual differences for tailor-made medicine for people living with HIV.

[Aim] Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. [Methods] As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. [Results] The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. [Conclusion] We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state., 脳機能結合の情報からギャンブル障害の判別器を開発 --人工知能技術の応用により診断や治療に新たな道!--. 京都大学プレスリリース. 2022-04-15.

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset., Measurement(s)mental or behavioural disorder • brain measurement • Demographic DataTechnology Type(s)functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance ImagingFactor Type(s)age • sex • site • disorderSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

Depressive disorders contribute heavily to global disease burden; This is possibly because patients are usually treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. On the contrary, treatment that directly influences the neural circuit relevant to an individual patient’s subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis, using fMRI functional connectivity neurofeedback to target a neural biomarker that objectively relates to a specific subset (melancholic) of depressive symptoms and that is generalizable across independent cohorts of patients. The targeted biomarker was the functional connectivity between the left dorsolateral prefrontal cortex and left precuneus, which has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. We found that the more a participant normalized this biomarker, the more related (brooding and more general depressive), but not unrelated (trait anxiety), symptoms were reduced. Thus, one-to-one correspondence between a normalized neural network and decreased depressive symptoms was demonstrated. These results were found in two experiments that took place several years apart by different experimenters, indicating their reproducibility. Indicative of their potential clinical utility, effects remained one-two months later.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second ‘most important’ FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Aim Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI); however, it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relation, the majority of neuroimaging studies has not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia (SCZ). Methods Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with SCZ and 37 matched healthy controls (HC). We conducted a functional connectivity (FC) analysis using independent component analysis and performed three group comparisons: (i) A-P versus P-A (all participants); (ii) SCZ versus HC for the A-P and P-A datasets; and (iii) the interaction between phase-encoding direction and participant group. Results The estimated FC differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although FC in the SCZ group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporoparietal junction and left fusiform gyrus. Conclusion Phase-encoding direction can influence the results of FC studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Recent studies examining electroconvulsive therapy (ECT) have reported that early sessions can induce rapid antidepressant and antipsychotic effects, and the early termination of ECT was reported to increase the risk of relapse. We hypothesized that different neural mechanisms associated with the therapeutic effects of ECT may be involved in the different responses observed during the early and late periods of ECT treatment. We investigated whether these antidepressant and antipsychotic effects were associated with temporally and spatially different regional gray matter volume (GMV) changes during ECT. Fourteen patients with major depressive disorder, with or without psychotic features, underwent 3-Tesla structural magnetic resonance imaging scans before (time point [Tp] 1), after the fifth or sixth ECT session (Tp2), and after ECT completion (Tp3). We investigated the regions in which GMV changed between Tp1 and Tp2, Tp2 and Tp3, and Tp1 and Tp3 using voxel-based morphometry. In addition, we investigated the association between regional GMV changes and improvement in depressive or psychotic symptoms. GMV increase in the left superior and inferior temporal gyrus during Tp1–Tp2 was associated with improvement in psychotic symptoms (P P

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., This study was conducted under the contract research Grant Number JP18dm0307008, JP17dm0107044 (“Development of BMI Technologies for Clinical Application” of the Strategic Research Program for Brain Sciences), JP18dm0307002, JP18dm0307004, and JP18dm0307009 supported by the Japan Agency for Medical Research and Development (AMED). This study was also partially supported by the ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). H.I. was partially supported by JSPS KAKENHI 26120002. A.Y. was partially supported by JSPS KAKENHI 15J06788.

Team sports activities are effective for improving the negative symptoms and cognitive functions in patients with schizophrenia. However, the interpersonal coordination during the sports and visual cognition of patients with schizophrenia who have team sports habits are unknown. The main objectives of this study were to test two hypotheses: first, patients with schizophrenia perform the skill requiring ball passing and receiving worse than healthy controls; and second, the patients will be impaired in these functionings in accordance with the previous studies regarding schizophrenia in general. Twelve patients with schizophrenia and 15 healthy controls, who had habits in football, participated in this study. The participants performed three conventional cognitive tests and a 3-vs-1 ball possession task to evaluate their interpersonal coordination. The results showed that in the 3-vs-1 possession task, the displacement in the pass angle for the patients was significantly smaller than that for the control. The recall in the complex figure test, the performance in the trail making test, and that in the five-choice reaction task for the patients were worse than those for the control. Moreover, we found the significant partial correlations in the patients between the extradimensional shift error and the pass angle as well as between the time in the trail making test and the displacement in the pass angle, whereas there was no significant correlation in the control group. This study clarified the impaired interpersonal coordination during team sports and the visual cognition of patients with schizophrenia who have team sports habits.

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual’s predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases., 複数の精神疾患における記憶力を共通のモデルで予測することに成功 --疾患に共通する認知機能低下のメカニズム解明に大きく前進--. 京都大学プレスリリース. 2019-01-11.

Functional magnetic resonance imaging (fMRI) neurofeedback can train subjects to control their brain activity with real-time processing and high spatial resolution, as many advances in MRI data acquisition methods, computer hardware, and processing algorithms have improved the sensitivity and speed of fMRI neurofeedback. FMRI neurofeedback has been applied to psychiatric disorders, including schizophrenia, major depressive disorder, attention-deficit hyperactivity disorder, and obsessive-compulsive disorder. Some studies reported that fMRI neurofeedback improved the symptoms of patients with psychiatric disorders, although it remains unclear if the regulation of the targeted brain regions or the functional connectivities themselves improved the symptoms. It is necessary that researchers pay enough attention to their study's design, because many non-specific factors such as introduction, mental strategies, self-efficacy, attention, motivation, learning ability, and reward influence the results in fMRI neurofeedback studies. If the long-term effect of fMRI neurofeedback on the symptoms of psychiatric disorders are recognized, fMRI neurofeedback will be useful in treating heterogeneous patients with psychiatric disorders without side effects.

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifier’s functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI); however, it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relation, the majority of neuroimaging studies has not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia (SCZ).Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with SCZ and 37 matched healthy controls (HC). We conducted a functional connectivity (FC) analysis using independent component analysis and performed three group comparisons: (i) A-P versus P-A (all participants); (ii) SCZ versus HC for the A-P and P-A datasets; and (iii) the interaction between phase-encoding direction and participant group.The estimated FC differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although FC in the SCZ group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporoparietal junction and left fusiform gyrus.Phase-encoding direction can influence the results of FC studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies.

It is widely known that there is a high prevalence of cigarette smoking in schizophrenia. One of the explanations is the self-medication hypothesis. Based on this hypothesis, it has been suggested that nicotine has procognitive effect or even neuroprotective effect in schizophrenia. However, cigarettes contain numerous neurotoxic substances, making the net effect of cigarette smoking on brain function and structure complex. Indeed, recent studies have called into question the self-medication hypothesis. We aimed to test whether there is an interaction between diagnosis and smoking status in gray matter volume, ie, whether smoking has specific effects on gray matter or whether main effects of these 2 variables additively affect common brain regions. Magnetic resonance imaging (MRI) images were obtained from 4 groups: (1) normal controls with no smoking history, (2) normal controls currently smoking and/or with a past history of smoking, (3) schizophrenia patients with no smoking history, and (4) schizophrenia patients currently smoking and/or with a past history of smoking. We used voxel-based morphometry to compare gray matter volumes among the 4 groups. We did not find any interaction between diagnosis and smoking, but we did find negative additive effects of schizophrenia diagnosis and smoking status in the left prefrontal cortex. The decrease in left prefrontal volume was associated with greater numbers of cigarette pack years and severe positive and negative symptoms. The current findings do not support the neuroprotective effect of smoking on gross brain structure in schizophrenia, emphasizing the necessity of longitudinal studies to test causal relationships among these variables.

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 recent approach for creating a theranostic biomarker, which consists mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (ii) 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., Comment: 46 pages, 5 figures

When making decisions in everyday life, we often have to choose between uncertain outcomes. Economic studies have demonstrated that healthy people tend to prefer options with known probabilities (risk) than those with unknown probabilities (ambiguity), which is referred to as "ambiguity aversion." However, it remains unclear how patients with schizophrenia behave under ambiguity, despite growing evidence of their altered decision-making under uncertainty. In this study, combining economic tools and functional magnetic resonance imaging (fMRI), we assessed the attitudes toward risk/ambiguity and investigated the neural correlates during decision-making under risk/ambiguity in schizophrenia. Although no significant difference in attitudes under risk was observed, patients with schizophrenia chose ambiguity significantly more often than the healthy controls. Attitudes under risk and ambiguity did not correlate across patients with schizophrenia. Furthermore, unlike in the healthy controls, activation of the left lateral orbitofrontal cortex was not increased during decision-making under ambiguity compared to under risk in schizophrenia. These results suggest that ambiguity aversion, a well-established subjective bias, is attenuated in patients with schizophrenia, highlighting the need to distinguish between risk and ambiguity when assessing decision-making under these situations. Our findings, comprising important clinical implications, contribute to improved understanding of the mechanisms underlying altered decision-making in patients with schizophrenia.

Brain β-amyloid (Aβ) deposition during normal aging is highlighted as an initial pathogenetic event in the development of Alzheimer's disease. Many recent brain imaging studies have focused on areas deactivated during cognitive tasks [the default mode network (DMN), i.e., medial frontal gyrus/anterior cingulate cortex and precuneus/posterior cingulate cortex], where the strength of functional coordination was more or less affected by cerebral Aβ deposits. In the present positron emission tomography study, to investigate whether regional glucose metabolic alterations and Aβ deposits seen in nondemented elderly human subjects (n= 22) are of pathophysiological importance in changes of brain hemodynamic coordination in DMN during normal aging, we measured cerebral glucose metabolism with [18F]FDG, Aβ deposits with [11C]PIB, and regional cerebral blood flow during control and working memory tasks by H215O on the same day. Data were analyzed using both region of interest and statistical parametric mapping. Our results indicated that the amount of Aβ deposits was negatively correlated with hemodynamic similarity between medial frontal and medial posterior regions, and the lower similarity was associated with poorer working memory performance. In contrast, brain glucose metabolism was not related to this medial hemodynamic similarity. These findings suggest that traceable Aβ deposition, but not glucose hypometabolism, in the brain plays an important role in occurrence of neuronal discoordination in DMN along with poor working memory in healthy elderly people.

Background Accumulating evidence suggests that dysregulation of the immune system is involved in the pathophysiology of autism spectrum disorders (ASD). The aim of the study was to explore immunological markers in peripheral plasma samples from non-medicated subjects with high-functioning ASD. Methodology/Principal Findings A multiplex assay for cytokines and chemokines was applied to plasma samples from male subjects with high-functioning ASD (n = 28) and matched controls (n = 28). Among a total of 48 analytes examined, the plasma concentrations of IL-1β, IL-1RA, IL-5, IL-8, IL-12(p70), IL-13, IL-17 and GRO-α were significantly higher in subjects with ASD compared with the corresponding values of matched controls after correction for multiple comparisons. Conclusion/Significance The results suggest that abnormal immune responses as assessed by multiplex analysis of cytokines may serve as one of the biological trait markers for ASD.

It is still unclear why some early Parkinson disease (PD) patients with unilateral parkinsonism develop bilateral parkinsonism soon after the diagnosis is made as Hoehn and Yahr (HY) stage 1 and others remain stable for a long time. Here, we examined in vivo changes in the brain dopaminergic system using PET with a dopamine transporter radiotracer, 11C-2-B-carbomethoxy-3B-(4-fluorophenyl) tropane (11C-CFT), to elucidate the pathophysiologic characteristics of the dopamine system in early converters. Methods: Twelve drug-naive PD patients with HY stage 1 disease and 8 age-matched healthy subjects participated in this study. Clinical evaluation of their parkinsonism was performed monthly until their HY stage 1 (unilateral parkinsonism) disease had become stage 2 (bilateral parkinsonism) disease according to the Unified Parkinson Disease Rating Scale. The endpoint of the follow-up study was the time of the conversion. Region-of-interest analysis was used to examine 11C-CFT binding in the mesocortical (nucleus accumbens, caudate, orbitofrontal cortex) and nigrostriatal (putamen) dopamine projection regions. Multiregression analyses between these PET data and clinical parameters were performed within the PD group. Results: Between-group comparisons showed that, irrespective of the duration of conversion, all PD patients clinically diagnosed at HY stage 1 had a significant reduction in 11C-CFT binding in the bilateral striatum (affected, −46%; unaffected, −35%). Regression analysis showed that the level of 11C-CFT binding in the nucleus accumbens and orbitofrontal cortex on the unaffected side was significantly positively correlated with the conversion interval. This positive correlation indicates that the more severe a dysfunction presents in the mesocortical dopamine system on the seemingly intact side, the more rapidly the parkinsonism proceeds to the intact side (bilateral parkinsonism). Conclusion: The finding of bilateral reduction in the striatal 11C-CFT binding even in HY stage 1 PD patients confirms that molecular changes in the dopamine system precede clinical phenotype, suggesting an advantage of PET for detecting an early abnormality of the disease. The spread of parkinsonism to the unaffected side soon after the diagnosis of HY stage 1 PD may be related to the degree of mesocortical dopamine dysfunction.

Amyloid β protein (Aβ) is known as a pathological substance in Alzheimer's disease (AD) and is assumed to coexist with a degree of activated microglia in the brain. However, it remains unclear whether these two events occur in parallel with characteristic hypometabolism in AD in vivo. The purpose of the present study was to clarify the in vivo relationship between Aβ accumulation and neuroinflammation in those specific brain regions in early AD.Eleven nootropic drug-naïve AD patients underwent a series of positron emission tomography (PET) measurements with [(11)C](R)PK11195, [(11)C]PIB and [(18)F]FDG and a battery of cognitive tests within the same day. The binding potentials (BPs) of [(11)C](R)PK11195 were directly compared with those of [(11)C]PIB in the brain regions with reduced glucose metabolism.BPs of [(11)C](R)PK11195 and [(11)C]PIB were significantly higher in the parietotemporal regions of AD patients than in ten healthy controls. In AD patients, there was a negative correlation between dementia score and [(11)C](R)PK11195 BPs, but not [(11)C]PIB, in the limbic, precuneus and prefrontal regions. Direct comparisons showed a significant negative correlation between [(11)C](R)PK11195 and [(11)C]PIB BPs in the posterior cingulate cortex (PCC) (p 0.05, corrected) that manifested the most severe reduction in [(18)F]FDG uptake.A lack of coupling between microglial activation and amyloid deposits may indicate that Aβ accumulation shown by [(11)C]PIB is not always the primary cause of microglial activation, but rather the negative correlation present in the PCC suggests that microglia can show higher activation during the production of Aβ in early AD.

Context A growing body of evidence suggests that aberrant immunologic systems underlie the pathophysiologic characteristics of autism spectrum disorder (ASD). However, to our knowledge, no information is available on the patterns of distribution of microglial activation in the brain in ASD. Objectives To identify brain regions associated with excessively activated microglia in the whole brain, and to examine similarities in the pattern of distribution of activated microglia in subjects with ASD and control subjects. Design Case-control study using positron emission tomography and a radiotracer for microglia—[ 11 C](R)-(1-[2-chrorophynyl]- N-methyl- N-[1-methylpropyl]-3 isoquinoline carboxamide) ([ 11 C](R)-PK11195). Setting Subjects recruited from the community. Participants Twenty men with ASD (age range, 18-31 years; mean [SD] IQ, 95.9 [16.7]) and 20 age- and IQ-matched healthy men as controls. Diagnosis of ASD was made in accordance with the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised. Main Outcome Measures Regional brain [ 11 C](R)-PK11195 binding potential as a representative measure of microglial activation. Results The [ 11 C](R)-PK11195 binding potential values were significantly higher in multiple brain regions in young adults with ASD compared with those of controls (P 11 C](R)-PK11195 binding potential values in these brain regions of ASD and control subjects was similar, whereas the magnitude of the [ 11 C](R)-PK11195 binding potential in the ASD group was greater than that of controls in all regions. Conclusions Our results indicate excessive microglial activation in multiple brain regions in young adult subjects with ASD. The similar distribution pattern of regional microglial activity in the ASD and control groups may indicate augmented but not altered microglial activation in the brain in the subjects with ASD.

Context Both neuropsychological and functional magnetic resonance imaging studies have shown deficiencies in face perception in subjects with autism spectrum disorders (ASD). The fusiform gyrus has been regarded as the key structure in face perception. The cholinergic system is known to regulate the function of the visual pathway, including the fusiform gyrus. Objectives To determine whether central acetylcholinesterase activity, a marker for the cholinergic system, is altered in ASD and whether the alteration in acetylcholinesterase activity, if any, is correlated with their social functioning. Design Using positron emission tomography and a radiotracer, N -[ 11 C]methyl-4-piperidyl acetate ([ 11 C]MP4A), regional cerebrocortical acetylcholinesterase activities were estimated by reference tissue–based linear least-squares analysis and expressed in terms of the rate constant k3 . Current and childhood autism symptoms in the adult subjects with ASD were assessed by the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised, respectively. Voxel-based analyses as well as region of interest–based methods were used for between-subject analysis and within-subject correlation analysis with respect to clinical variables. Setting Participants recruited from the community. Participants Twenty adult subjects with ASD (14 male and 6 female; age range, 18-33 years; mean [SD] intelligence quotient, 91.6 [4.3]) and 20 age-, sex-, and intelligence quotient–matched healthy controls. Results Both voxel- and region of interest–based analyses revealed significantly lower [ 11 C]MP4A k3 values in the bilateral fusiform gyri of subjects with ASD than in those of controls ( P k3 values in subjects with ASD were negatively correlated with their social disabilities as assessed by Autism Diagnostic Observation Schedule as well as Autism Diagnostic Interview–Revised. Conclusions The results suggest that a deficit in cholinergic innervations of the fusiform gyrus, which can be observed in adults with ASD, may be related to not only current but also childhood impairment of social functioning.

BackgroundInvestigation into the whole brain morphology of early onset schizophrenia (EOS) to date has been sparse. We studied the regional brain volumes in EOS patients, and the correlations between regional volume measures and symptom severity.MethodsA total of 18 EOS patients (onset under 16 years) and 18 controls matched for age, gender, parental socioeconomic status, and height were examined. Voxel-based morphometric analysis using the Brain Analysis Morphological Mapping (BAMM) software package was employed to explore alterations of the regional grey (GM) and white matter (WM) volumes in EOS patients. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).ResultsEOS patients had significantly reduced GM volume in the left parahippocampal, inferior frontal, and superior temporal gyri, compared with the controls. They also had less WM volume in the left posterior limb of the internal capsule and the left inferior longitudinal fasciculus. The positive symptom score of PANSS (higher values corresponding to more severe symptoms) was negatively related to GM volume in the bilateral posterior cingulate gyrus. The negative symptom score was positively correlated with GM volume in the right thalamus. As for the association with WM volume, the positive symptom score of PANSS was positively related to cerebellar WM (vermis region), and negatively correlated with WM in the brain stem (pons) and in the bilateral cerebellum (hemisphere region).ConclusionOur findings of regional volume alterations of GM and WM in EOS patients coincide with those of previous studies of adult onset schizophrenia patients. However, in brain regions that had no overall structural differences between EOS patients and controls (that is, the bilateral posterior cingulate gyrus, the right thalamus, the cerebellum, and the pons), within-subject analysis of EOS patients alone revealed that there were significant associations of the volume in these areas and the symptom severity. These findings suggest that at an early stage of the illness, especially for those with onset before brain maturation, a wide range of disturbed neural circuits, including these brain regions that show no apparent morphological changes, may contribute to the formation of the symptomatology.