13C-phenylalanine breath test detects altered phenylalanine kinetics in schizophrenia patients

Phenylalanine is an essential amino acid required for the synthesis of catecholamines including dopamine. Altered levels of phenylalanine and its metabolites in blood and cerebrospinal fluid have been reported in schizophrenia patients. This study attempted to examine for the first time whether phenylalanine kinetics is altered in schizophrenia using L-[1-13C]phenylalanine breath test (13C-PBT). The subjects were 20 chronically medicated schizophrenia patients (DSM-IV) and the same number of age- and sex-matched controls. 13C-phenylalanine (99 atom% 13C; 100 mg) was administered orally and the breath 13CO2 /12CO2 ratio was monitored for 120 min. The possible effect of antipsychotic medication (risperidone (RPD) or haloperidol (HPD) treatment for 21 days) on 13C-PBT was examined in rats. Body weight (BW), age and diagnostic status were significant predictors of the area under the curve of the time course of Δ13CO2 (‰) and the cumulative recovery rate (CRR) at 120 min. A repeated measures analysis of covariance controlled for age and BW revealed that the patterns of CRR change over time differed between the patients and controls and that Δ13CO2 was lower in the patients than in the controls at all sampling time points during the 120 min test, with an overall significant difference between the two groups. Chronic administration of RPD or HPD had no significant effect on 13C-PBT indices in rats. Our results suggest that 13C-PBT is a novel laboratory test that can detect altered phenylalanine kinetics in chronic schizophrenia patients. Animal experiments suggest that the observed changes are unlikely to be attributable to antipsychotic medication

Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI—Virtual Partner Interaction—a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Measuring macroscopic brain connections in vivo

Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means