Pituitary volume in schizophrenia spectrum disorders

Introduction—There is converging evidence supporting hyperactivity of the HypothalamicPituitary-Adrenal (HPA) axis in schizophrenia spectrum disorders (SSD), such as schizotypal personality disorder (SPD), first-episode schizophrenia (FESZ) and chronic schizophrenia (CHSZ). Such an aberrant HPA activity might have volumetric consequences on the pituitary gland. However, previous magnetic resonance imaging (MRI) studies assessing pituitary volume (PV) in SSD are conflicting. The main objective of this study was to examine further PV in SSD. Methods—PV were manually traced on structural MRIs in 137 subjects, including subjects with SPD (n=40), FESZ (n=15), CHSZ (n=15), and HC (n=67). We used an ANCOVA to test PV between groups and gender while controlling for inter-subject variability in age, years of education, socioeconomic status, and whole brain volume. Results—Overall, women had larger PV than men, and within the male sample all SSD subjects had smaller PV than HC, statistically significant only for the SPD group. In addition, dose of medication, illness duration and age of onset were not associated with PV. Conclusion—Chronic untreated HPA hyperactivity might account for smaller PV in SPD subjects, whereas the absence of PV changes in FESZ and CHSZ patients might be related to the normalizing effects of antipsychotics on PV. SPD studies offer a way to examine HPA related alterations in SSD without the potential confounds of medication effects

S.P.A.M. Fighting SPAM

On Twitch.tv, streamers encounter issues where human moderators must continuously monitor live channels to prevent inappropriate discussion. Additionally the streamers are not able to take advantage of the rapid stream of information coming from their viewers. These problems stem from the large amount of data that is difficult for humans to process and are much more suited for a programmatic solution. Our system will allow streamers on Twitch.tv to apply automatic moderation to their streaming channel and will give insights into viewer trends and information. Currently, systems exist to solve similar problems but rely on human interaction to moderate channels or very limited bot interactions and provide only big picture statistical information. Our bot interacts with the Twitch.tv IRC channel, reading user input and server messages to determine previous actions taken against users as good or bad, learn from said actions, and be able to make accurate moderating actions. We are in the process of scrubbing chats and working through IRC logs to be able to train the bot to react properly to our specified criteria. In addition we have a preliminary classifier running that allows us to make judgements based on certain user and chat message statistics. In this paper we will detail the methods used to collect, label, and learn from the information gathered in addition to the methods of providing statistics. Need for human intervention to moderate and parse the constant streams of data that go through Twitch.tv motivated us to automate parts of the process. The kinds of statistics taken from streams, users, and channels allow us to take advantage of machine learning techniques to provide an enhanced experience for all

S.P.A.M. Fighting SPAM

On Twitch.tv, streamers encounter issues where human moderators must continuously monitor live channels to prevent inappropriate discussion. Additionally the streamers are not able to take advantage of the rapid stream of information coming from their viewers. These problems stem from the large amount of data that is difficult for humans to process and are much more suited for a programmatic solution. Our system will allow streamers on Twitch.tv to apply automatic moderation to their streaming channel and will give insights into viewer trends and information. Currently, systems exist to solve similar problems but rely on human interaction to moderate channels or very limited bot interactions and provide only big picture statistical information. Our bot interacts with the Twitch.tv IRC channel, reading user input and server messages to determine previous actions taken against users as good or bad, learn from said actions, and be able to make accurate moderating actions. We are in the process of scrubbing chats and working through IRC logs to be able to train the bot to react properly to our specified criteria. In addition we have a preliminary classifier running that allows us to make judgements based on certain user and chat message statistics. In this paper we will detail the methods used to collect, label, and learn from the information gathered in addition to the methods of providing statistics. Need for human intervention to moderate and parse the constant streams of data that go through Twitch.tv motivated us to automate parts of the process. The kinds of statistics taken from streams, users, and channels allow us to take advantage of machine learning techniques to provide an enhanced experience for all

Cingulum bundle integrity associated with delusions of control in schizophrenia: Preliminary evidence from diffusion-tensor tractography

Background—Delusions of control are among the most distinctive and characteristic symptoms of schizophrenia. Several theories have been proposed that implicate aberrant communication between spatially disparate brain regions in the etiology of this symptom. Given that white matter fasciculi represent the anatomical infrastructure for long-distance communication in the brain, the present study investigated whether delusions of control were associated with structural abnormalities in four major white matter fasciculi. Methods—Ten schizophrenia patients with current delusions of control, 13 patients with no clinical history of delusions of control, and 12 healthy controls underwent a Diffusion-Tensor Imaging (DTI) scan. Deterministic tractography was used to extract the corpus callosum, superior longitudinal fasciculus, arcuate fasciculus and cingulum bundle. The structural integrity of these four fasciculi were quantified with Fractional Anisotropy (FA), and compared between groups. Results—The patients with delusions of control exhibited significantly lower FA in all four fasciculi, relative to the healthy controls. Furthermore, the patients with delusions of control also exhibited significantly lower FA in the cingulum bundle relative to patients without a history of this symptom, and this difference remained significant when controlling for between-group differences in global SAPS score and medication dosage. Conclusions—The results suggest that structural damage to the cingulum bundle may be involved in the etiology of delusions of control, possibly because of its role in connecting the action initiation areas of the premotor cortex with the cingulate gyrus

Decreased axial diffusivity within language connections: A possible biomarker of schizophrenia risk

Siblings of patients diagnosed with schizophrenia are at elevated risk for developing this disorder. The nature of such risk associated with brain abnormalities, and whether such abnormalities are similar to those observed in schizophrenia, remain unclear. Deficits in language processing are frequently reported in increased risk populations. Interestingly, white matter pathology involving fronto-temporal language pathways, including Arcuate Fasciculus (AF), Uncinate Fasciculus (UF), and Inferior Occipitofrontal Fasciculus (IOFF), are frequently reported in schizophrenia. In this study, high spatial and directional resolution diffusion MRI data was obtained on a 3T magnet from 33 subjects with increased familial risk for developing schizophrenia, and 28 control subjects. Diffusion Tractography was performed to measure white matter integrity within AF, UF, and IOFF. To understand these abnormalities, Fractional anisotropy (FA, a measure of tract integrity) and Trace (a measure of overall diffusion), were combined with more specific measures of axial diffusivity (AX, a putative measure of axonal integrity) and radial diffusivity (RD, a putative measure of myelin integrity). Results revealed a significant decrease in Trace within IOFF, and a significant decrease in AX in all tracts. FA and RD anomalies, frequently reported in schizophrenia, were not observed. Moreover, AX group effect was modulated by age, with increased risk subjects demonstrating a deviation from normal maturation trajectory. Findings suggest that familial risk for schizophrenia may be associated with abnormalities in axonal rather than myelin integrity, and possibly associated with disruptions in normal brain maturation. AX should be considered a possible biomarker of risk for developing schizophrenia