Background Corpus Research Articles

The clinical features of patients with seizure freedom and failure after total corpus callosotomy for childhood-onset refractory epilepsy

Background Corpus callosotomy is a palliative surgery for medically refractory epilepsy. We aim to analyze the clinical features of patients with seizure freedom and failure after total corpus callosotomy for childhood-onset refractory epilepsy. Methods We retrospectively reviewed the clinical courses of patients with childhood-onset refractory epilepsy undergoing total corpus callosotomy between May 2009 and March 2019. Seizure outcome at the last follow-up was the primary outcome. The clinical features of patients with seizure freedom and failure after callosotomy were compared. Results Eighty patients with childhood-onset refractory epilepsy underwent total corpus callosotomy; 15 (18.8%) obtained freedom from all seizures and 19 (23.8%) had unworthwhile improvement and failure. The mean ages at seizure onset in patients with seizure freedom and failure after callosotomy were 5.7 and 5.9 years; and mean seizure durations were 9.4 and 11.5 years, respectively. Univariate analysis found epilepsy syndrome (p = 0.047), mental retardation (p = 0.007), previous medical history (p = 0.004), ≥10 seizures per day (p = 0.024), theta waves in the background electroencephalogram (p = 0.024), and acute postoperative seizure (p = 0.000) were associated with failure after callosotomy. Seizure freedom after callosotomy was more common among patients with less than 10 seizures per day. Conclusions Total corpus callosotomy is an effective palliative procedure for childhood-onset refractory epilepsy, particularly for patients with specific clinical characteristics. Callosotomy has a high seizure-free rate in well-selected patients.

Reasoning over Public and Private Data in Retrieval-Based Systems

Abstract Users an organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private context is important to personalize open-domain tasks such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve information that is relevant to an input question from a background corpus before producing an answer. While today’s retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We define the Split Iterative Retrieval (SPIRAL) problem involving iterative retrieval over multiple privacy scopes. We introduce a foundational benchmark with which to study SPIRAL, as no existing benchmark includes data from a private distribution. Our dataset, ConcurrentQA, includes data from distinct public and private distributions and is the first textual QA benchmark requiring concurrent retrieval over multiple distributions. Finally, we show that existing retrieval approaches face significant performance degradations when applied to our proposed retrieval setting and investigate approaches with which these tradeoffs can be mitigated. We release the new benchmark and code to reproduce the results.1

COVID‑19 and a Greek Catholic Hierarch’s Experience of Virtual Communication with Parishioners

In this article, the author carries out a communicative and pragmatic analysis of texts and a stylistic analysis of the keywords of religious discourse. The object of the research is the discourse of the Greek Catholic Church in Slovakia. The subject of the research is the keywords and main topics in the corpus of Archbishop Milan Chautur’s publications on social networks. The research methodology is based on the principles of discourse analysis, computational linguistics, linguistics 2.0, and quantitative linguistics. To realise the purpose of the research, the author uses the linguistic tool Sketch Engine, a fourth-generation concordancer, which provides quick search and statistical processing of data. For the corpus, the author refers to material from Chautur’s Internet messages in the profile named “Vladyka Milan odpovedá” (Vladyka Chautur responds) over one year of the pandemic, i. e., between 2020 and 2021. Using the Simple Maths method, the author extracts keywords based on two corpora – the main one (created by the user) and the background one (built into the system). Vladyka’s web-communication fits the modern trend of active communication through social networks, where it is not only used by representatives of the secular world, but also by the clergy. Considering the fact that the background corpus of Slovak Web 2011 (skTenTen11) is a language model, the author singles out the following keywords: isus (Jesus), Christ (Christ), pandémia (pandemic), kovid (Covid), bohorodička (Virgin Mary), cerkov (Church), vakcinácium (vaccinacium), and eparchia (diocese). These words have the highest coefficient of semantic similarity (Score) of words from the studied and background corpora. Using the linguistic criterion for isolating keywords, it is possible to single out the cognitive components of the archbishop’s discursive field, such as discursive dominants: human life, the Church on earth and in heaven, family.

Prenatal genetic testing in 19 fetuses with corpus callosum abnormality.

BackgroundCorpus callosum abnormality (CCA) can lead to epilepsy, moderate severe neurologic or mental retardation. The prognosis of CCA is closely related to genetic etiology. However, copy number variations (CNVs) associated with fetal CCA are still limited and need to be further identified. Only a few scattered cases have been reported to diagnose CCA by whole exome sequencing (WES).MethodsKaryotyping analysis, copy number variation sequencing (CNV‐seq), chromosomal microarray analysis (CMA) and WES were parallelly performed for prenatal diagnosis of 19 CCA cases.ResultsThe total detection rate of karyotyping analysis, CMA (or CNV‐seq) and WES were 15.79% (3/19), 21.05% (4/19) and 40.00% (2/5), respectively. Two cases (case 11 and case 15) were diagnosed as aneuploidy (47, XY, + 13 and 47, XX, + 21) by karyotyping analysis and CNV‐seq. Karyotyping analysis revealed an unknown origin fragment (46,XY,add(13)(p11.2)) in case 3, which was further confirmed to originate from p13.3p11.2 of chromosome 17 by CNV‐seq. CMA revealed arr1q43q44 (238923617–246964774) × 1(8.04 Mb) in case 8 with a negative result of chromosome karyotype. WES revealed that 2 of 5 cases with negative results of karyotyping and CNV‐seq or CMA carried pathogenic genes ALDH7A1 and ARID1B.ConclusionParallel genetic tests showed that CNV‐seq and CMA are able to identify additional, clinically significant cytogenetic information of CCA compared to karyotyping; WES significantly improves the detection rate of genetic etiology of CCA. For the patients with a negative results of CNV‐seq or CMA, further WES test is recommended.

An exploratory study of COVID-19 misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.

Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine. In a prospective study of 553 MS patients with 704 acquisitions, 200 unique 2D T2 -weighted MRI scans were delineated to develop, train, and validate DeepnCCA. Comparative FreeSurfer segmentations were obtained in 504 3D T1 -weighted scans. Both FreeSurfer and DeepnCCA outputs were correlated with clinical disability. Using principal component analysis of the DeepnCCA output, the morphological changes were explored in relation to clinical disease burden. DeepnCCA and manual segmentations had high similarity (Dice coefficients 98.1 .11%, 89.3 .76%, for intracranial and corpus callosum area, respectively through 10-fold cross-validation). DeepnCCA had numerically stronger correlations with cognitive and physical disability as compared to FreeSurfer: Expanded disability status scale (EDSS) ±6 months (r = -.22 P = .002; r = -.17, P = .013), future EDSS (r = -.26, P<.001; r = -.17, P = .012), and future symbol digit modalities test (r = .26, P = .001; r = .24, P = .003). The corpus callosum became thinner with increasing cognitive and physical disability. Increasing physical disability, additionally, significantly correlated with a more angled corpus callosum. DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.

Ensemble topic modeling using weighted term co-associations

Topic modeling is a popular unsupervised technique that is used to discover the latent thematic structure in text corpora. The evaluation of topic models typically involves measuring the semantic coherence of the terms describing each topic, where a single value is used to summarize the quality of an overall model. However, this can create difficulties when one seeks to interpret the strengths and weaknesses of a given topic model. With this in mind, we propose a new ensemble topic modeling approach that incorporates both stability information, in the form of term co-associations, and semantic similarity information, as derived from a word embedding constructed on a background corpus. Our evaluations show that this approach can simultaneously yield higher quality models when considering the produced topic descriptors and document-topic assignments, while also facilitating the comparison and evaluation of solutions through the visualization of the discovered topical structure, the ordering of the topic descriptors, and the ranking of term pairs which appear in topic descriptors.

On detecting urgency in short crisis messages using minimal supervision and transfer learning.

Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messages as indicating an urgent need. The problem is challenging not just because of the sparseness of data in the immediate aftermath of a disaster, but because of the varying characteristics of disasters in developing countries (making it difficult to train just one system) and the noise and quirks in social media. In this paper, we present a robust, low-supervision social media urgency system that adapts to arbitrary crises by leveraging both labeled and unlabeled data in an ensemble setting. The system is also able to adapt to new crises where an unlabeled background corpus may not be available yet by utilizing a simple and effective transfer learning methodology. Experimentally, our transfer learning and low-supervision approaches are found to outperform viable baselines with high significance on myriad disaster datasets.

Transfer learning for multimodal dialog

Audio-Visual Scene-Aware Dialog (AVSD) is best understood as an extension of Visual Question Answering, the task of generating a textual answer in response to a textual question on multi-media content. In AVSD, the answer-relevant “context” is expanded to include past dialog turns, which we view as a specialized form of extra textual knowledge (in addition to the standard video features). We have developed a framework that uses hierarchical attention to fuse contributions from different modalities, and had shown how it can be used to generate textual summaries from multi-modal sources, specifically videos with accompanying commentary. In this paper, we transfer the algorithmic approach, models, and data from this background corpus of 2000 h of how-to videos to the AVSD task, and report our findings. Our approach uses dialog context, but makes no assumption about the ordering of the history. Our system achieves the best performance in both automatic and human evaluations in the 7th Dialog State Tracking Challenge (AVSD).

MRI-Based Manual versus Automated Corpus Callosum Volumetric Measurements in Multiple Sclerosis.

Corpus callosum atrophy is a neurodegenerative biomarker in multiple sclerosis (MS). Manual delineations are gold standard but subjective and labor intensive. Novel automated methods are promising but require validation. We aimed to compare the robustness of manual versus automatic corpus callosum segmentations based on FreeSurfer. Nine MS patients (6 females, age 38±13 years, disease duration 7.3±5.2 years) were scanned twice with repositioning using 3-dimensional T1 -weighted magnetic resonance imaging on three scanners (two 1.5 T and one 3.0 T), that is, six scans/patient, on the same day. Normalized corpus callosum areas were measured independently by a junior doctor and neuroradiologist. The cross-sectional and longitudinal streams of FreeSurfer were used to segment the corpus callosum volume. Manual measurements had high intrarater (junior doctor .96 and neuroradiologist .96) and interrater agreement (.94), by intraclass correlation coefficient (P<.001). The coefficient of variation was lowest for longitudinal FreeSurfer (.96% within scanners; 2.0% between scanners) compared to cross-sectional FreeSurfer (3.7%, P = .001; 3.8%, P = .058) and the neuroradiologist (2.3%, P = .005; 2.4%, P = .33). Longitudinal FreeSurfer was also more accurate than cross-sectional (Dice scores 83.9±7.5% vs. 78.9±8.4%, P<.01 relative to manual segmentations). The corpus callosum measures correlated with physical disability (longitudinal FreeSurfer r = -.36, P<.01; neuroradiologist r = -.32, P<.01) and cognitive disability (longitudinal FreeSurfer r = .68, P<.001; neuroradiologist r = .64, P<.001). FreeSurfer's longitudinal stream provides corpus callosum measures with better repeatability than current manual methods and with similar clinical correlations. However, due to some limitations in accuracy, caution is warranted when using FreeSurfer with clinical data.

HiTR: Hierarchical Topic Model Re-Estimation for Measuring Topical Diversity of Documents

A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents. Topic models play a central role in this approach and, hence, their quality is crucial to the efficacy of measuring topical diversity. The quality of topic models is affected by two causes: generality and impurity of topics. General topics only include common information of a background corpus and are assigned to most of the documents. Impure topics contain words that are not related to the topic. Impurity lowers the interpretability of topic models. Impure topics are likely to get assigned to documents erroneously. We propose a hierarchical re-estimation process aimed at removing generality and impurity. Our approach has three re-estimation components: (1) document re-estimation, which removes general words from the documents; (2) topic re-estimation, which re-estimates the distribution over words of each topic; and (3) topic assignment re-estimation, which re-estimates for each document its distributions over topics. For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.

Fiber Tractography and Diffusion Tensor Imaging in Children with Agenesis and Dysgenesis of Corpus Callosum: A Clinico-Radiological Correlation

Background Corpus callosum is the largest commissure in human brain. It consists of tightly packed white matter tracts connecting the two cerebral hemispheres. In this study we aimed to evaluate role of fiber tractography (FT), and diffusion tensor imaging (DTI) in pediatric patients with corpus callosum anomalies and correlate findings with clinical presentation. Materials and Methods This prospective study included 115 children with clinical presentations and CT findings suggested corpus callosum anomalies. Their ages ranged between 2 and 12 year-old. They referred from Pediatric Neurology unit to Radiology Department in Minia University hospital for children, Minia (Egypt) from April 2018 till December 2018. They underwent MRI fiber tractography and diffusion tensor imaging after approval of ethical committee of our institution and informed consent form patients’ parents. Results Our study included 42 (36.5 %) males and 73 (63.5 %) females. They were reviewed for corpus callosum anomalies according to Hanna classification, the commonest was hypoplasia without dysplasia 37/115 (32 %). Using DTI and FT, corpus callosum fiber defects were classified into fronto-rostral, caudal and whole body defect. Significant statistical correlation was found between ADHD, autism and loss of fronto-rostral fibers. Epilepsy and developmental delay were correlated to whole body defect and caudal fiber tract defects. Conclusion Based on the results, Fiber tractography and diffusion tensor imaging are newly developed promising techniques. They proved high accuracy in localization of exact affected corpus callosum white matter tract. They help to predict prognosis of patients that could not be explained by morphological changes seen in conventional MRI.

Prevalence and risk factors for fundic gland polyps

The incidence of fundic gland polyps (FGPs) is increasing rapidly and has surpassed hyperplastic polyps to become the most common epithelial gastric polyps in Western countries and China.1, 2 They are usually multiple, small (1-5 mm), transparent, and sessile; the background gastric mucosa is typically normal.3 FGPs are more prevalent among females and are often located in the gastric fundus and body. There are two types of FGPs: sporadic and syndromic (in association with familial adenomatosis polyposis). Although different in their genetic and molecular backgrounds, sporadic and syndromic FGPs are histologically and histochemically indistinguishable. Syndromic FGPs occur in the presence of inherited germline mutation in the adenomatous polyposis coli (APC) gene plus the acquisition of a somatic mutation, leading to complete inactivation of the APC tumor suppressor gene.4 Familial adenomatosis polyposis should be excluded by colonoscopy if FGPs are numerous, large (>1 cm), or dysplasic.3 For sporadic FGPs, up to 23% are associated with proton pump inhibitor (PPI) use.5, 6 A recent review with meta-analysis indicated that long-term use of PPIs (≥12 months) increased the risk of FGPs (pooled odds ratio [confidence interval]: 3.81 [2.78-5.24] for the fixed effects model and 2.88 [0.97-8.55] for the random effects model).7 The risk was even higher if the exposure duration was more than 48 months (pooled odds ratio: 4.02). There is no or inverse association with Helicobacter pylori infection.1, 3 FGPs rarely harbor dysplastic lesions. A study in the USA of 35 372 FGPs found that only 0.3% had low-grade dysplasia.5 Moreover, even syndromic FGPs rarely progress to cancer.8 Once epithelial gastric polyps are found at initial endoscopy, it is important to sample polyps and/or normal-appearing gastric mucosa to establish histological diagnosis and to define who needs surveillance. Biopsies from the background antrum and corpus mucosa could rule out atrophic gastritis, intestinal metaplasia, dysplasia, and H. pylori infection. All these information is important for the management of other epithelial gastric polyps, including hyperplastic polyp, adenoma, and type 1 or 2 gastric neuroendocrine tumors.3 Once sporadic FGPs are diagnosed, routine endoscopic surveillance is not recommended in the absence of dysplasia. There is still no consensus on the indication of endoscopy resection of sporadic FGPs. However, many experts suggest the removal of FGPs larger than 10 mm.3 Few studies have considered risk factors other than age, gender, PPIs, and H. pylori infection for sporadic FGPs. A recent one-center, endoscopy-based retrospective study in Taiwan indicated the incidence of FGPs increased from 0.6% in 2010 to 1.3% in 2015.9 The controls were age- and gender-matched subjects who received upper endoscopy during the similar period with the cases. The authors found the presence of liver cirrhosis, gastric ulcer, duodenal ulcer (adjusted odds ratio: 0.18, 0.28, and 0.35, respectively, all P < 0.001), but not reflux esophagitis, were inversely associated with FGP risk. FGPs are commonly seen in patients with relatively healthy gastric mucosa. This is an interesting finding that implies causative factors to gastric mucosa, including cirrhosis and peptic ulcers, which could reduce the formation of FGPs, probably through the facilitation of glandular outflow. However, more than 30% of H. pylori status was missing, and the information of PPI exposure was less than adequate in this study. Future studies are necessary to support this finding and the proposed mechanism behind it. The author declares no conflict of interest.

Corpus callosal abnormalities in dogs.

BackgroundCorpus callosal abnormalities (CCA) in dogs have been only sporadically reported and are poorly characterized.Hypothesis/ObjectivesTo describe the clinical presentation and magnetic resonance imaging (MRI) characteristics of dogs with CCA.AnimalsFifteen client‐owned dogs.MethodsRetrospective study. Records of the contributing institutions were reviewed to identify dogs diagnosed with malformations affecting the corpus callosum (CC); cases in which the CCA was thought to be secondary were excluded.ResultsThe most represented breeds were Staffordshire Bull Terriers (5/15) and Miniature Schnauzers (3/15; n = 3, 20%) and the mean age at time of presentation of 19 months (range 3–81 months). The clinical signs most commonly reported were adipsia/hypodipsia with associated hypernatremia (12/15), tremors (6/15), and seizures (6/15). Review of the MR images revealed that 10 dogs had absence of the rostral CC and hypoplasia of the caudal portion, 4 dogs had a diffusely hypoplastic and dysplastic CC, and 1 dog had a diffusely hypoplastic CC. In 14 cases, there was abnormal cortical development with fusion of the ventral frontal lobes and part of the diencephalon, indicating lobar holoprosencephaly.Conclusions and Clinical ImportancePrevious literature has mainly associated CCA with adipsia and only 12 of 15 dogs in the current series demonstrated this abnormality. There are different degrees of the malformation but in 10 dogs the rostral portion of the CC is most severely affected. Fourteen dogs have simultaneous fusion of the midline structures rostral to the CC; this region has several structures involved in thirst regulation and might explain this derangement.

Favoring, Likelihoodism, and Bayesianism

Strictly speaking, favoring should really be thought of as a four-place relation, between E, H1, H2, and a corpus of background evidence K. But, for present purposes (which won't address issues involving K), I will suppress the background corpus, so as to simplify our discussion. Moreover, the favoring relation is meant to be a propositional epistemic relation, as opposed to a doxastic epistemic relation. That is, the favoring relation is not meant to be restricted to bodies of evidence that are possessed (as evidence) by some actual agent(s), or to hypotheses that are (in fact) entertained by some actual agent(s). In this sense, favoring is analogous to the relation of propositional justification — as opposed to doxastic justification (Conee 1980). In order to facilitate a comparison of Likelihoodist vs Bayesian explications of favoring, I will presuppose the following bridge principle, linking favoring and evidential support:

An Ontology Learning Method Based on Document Clustering

Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

Measuring mono-word termhood by rank difference via corpus comparison

Terminology as a set of concept carriers crystallizes our special knowledge about a subject. Automatic term recognition (ATR) plays a critical role in the processing and management of various kinds of information, knowledge and documents, e.g., knowledge acquisition via text mining. Measuring termhood properly is one of the core issues involved in ATR. This article presents a novel approach to termhood measurement for mono-word terms via corpus comparison, which quantifies the termhood of a term candidate as its rank difference in a domain and a background corpus. Our ATR experiments to identify legal terms in Hong Kong (HK) legal texts with the British National Corpus (BNC) as background corpus provide evidence to confirm the validity and effectiveness of this approach. Without any prior knowledge and ad hoc heuristics, it achieves a precision of 97.0% on the top 1000 candidates and a precision of 96.1% on the top 10% candidates that are most highly ranked by the termhood measure, illustrating a state-of-the-art performance on mono-word ATR in the field.

Feature Space Mahalanobis Sequence Kernels: Application to SVM Speaker Verification

The generalized linear discriminant sequence (GLDS) kernel has been shown to provide very good performance and efficiency at the NIST Speaker Recognition Evaluations (SRE) in the last few years. This kernel is based on an explicit map of polynomial expansions of input frames which, because of practical limitations, have to be of a degree less or equal to three. In this paper, we consider an extension of the GLDS kernel to allow not only any polynomial degree but also any embedding, including infinite-dimensional ones associated with Mercer kernels (such as Gaussian kernels). It turns out that the resulting kernels belong to the family of posterior covariance kernels. However, their exact ldquokernelizedrdquo form involves the computation of the Gram matrix on background data, and may be intractable when the background corpus is very large (which is the case in speaker verification). To overcome this problem, we use a low-rank approximation of the Gram matrix to provide an approximate but tractable form of these kernels. We then present comparative experiments on NIST SRE 2005. The results show that our sequence kernel outperforms the GLDS one, and gives similar (individual) performances to the traditional universal background model-Gaussiam mixture model (UBM-GMM) system. As expected, the fusion of both improves the scores.

Investigation of corpus callosum in schizophrenia with diffusion imaging

Background Corpus callosum (CC) is the main white matter commissure between the two cerebral hemispheres. Abnormalities of CC have been shown in schizophrenia patients by magnetic resonance imaging (MRI) studies. We here further investigated CC organization with diffusion imaging (DWI) in a sample of schizophrenia patients recruited from the epidemiologically defined catchment area of South Verona, Italy. Methods Sixty-seven patients with schizophrenia and 70 normal controls were studied. Regions of interests (ROIs), standardized at 5 pixels, were placed in CC on the non-diffusion weighted echoplanar images ( b = 0) and were then automatically transferred to the corresponding maps to obtain the apparent diffusion coefficient (ADC) of water molecules. Results ADC measures for all callosal subregions in schizophrenia patients were significantly greater compared to normal controls (ANCOVA, p < 0.05). Positive symptoms significantly correlated with anterior callosal ADC measures (partial correlation analyses, p < 0.05). Conclusions These findings support the existence of widespread microstructure disruption of CC in schizophrenia, which may ultimately lead to inter-hemispheric misconnection, and also suggest a specific role of anterior transcallosal disconnectivity in underlying positive symptoms. Future longitudinal MRI studies in high risk and first-episode patients together with neurophysiological tests are indicated to further examine CC anatomical abnormalities and inter-hemispheric transmission in schizophrenia.