An integrated brain-behavior model for working memory.

Working memory (WM) is a central construct in cognitive neuroscience because it comprises mechanisms of active information maintenance and cognitive control that underpin most complex cognitive behavior. Individual variation in WM has been associated with multiple behavioral and health features including demographic characteristics, cognitive and physical traits and lifestyle choices. In this context, we used sparse canonical correlation analyses (sCCAs) to determine the covariation between brain imaging metrics of WM-network activation and connectivity and nonimaging measures relating to sensorimotor processing, affective and nonaffective cognition, mental health and personality, physical health and lifestyle choices derived from 823 healthy participants derived from the Human Connectome Project. We conducted sCCAs at two levels: a global level, testing the overall association between the entire imaging and behavioral-health data sets; and a modular level, testing associations between subsets of the two data sets. The behavioral-health and neuroimaging data sets showed significant interdependency. Variables with positive correlation to the neuroimaging variate represented higher physical endurance and fluid intelligence as well as better function in multiple higher-order cognitive domains. Negatively correlated variables represented indicators of suboptimal cardiovascular and metabolic control and lifestyle choices such as alcohol and nicotine use. These results underscore the importance of accounting for behavioral-health factors in neuroimaging studies of WM and provide a neuroscience-informed framework for personalized and public health interventions to promote and maintain the integrity of the WM network

Delocalization transition for the Google matrix

We study the localization properties of eigenvectors of the Google matrix, generated both from the World Wide Web and from the Albert-Barabasi model of networks. We establish the emergence of a delocalization phase for the PageRank vector when network parameters are changed. In the phase of localized PageRank, a delocalization takes place in the complex plane of eigenvalues of the matrix, leading to delocalized relaxation modes. We argue that the efficiency of information retrieval by Google-type search is strongly affected in the phase of delocalized PageRank.Comment: 4 pages, 5 figures. Research done at http://www.quantware.ups-tlse.fr

Dissociable auditory mismatch response and connectivity patterns in adolescents with schizophrenia and adolescents with bipolar disorder with psychosis: A magnetoencephalography study

BACKGROUND: There is overlap between schizophrenia and bipolar disorder regarding genetic risk as well as neuropsychological and structural brain deficits. Finding common and distinct event-response potential (ERP) responses and connectivity patterns may offer potential biomarkers to distinguish the disorders. OBJECTIVE: To examine the neuronal auditory response elicited by a roving mismatch negativity (MMN) paradigm using magnetoencephalography (MEG). PARTICIPANTS: 15 Adolescents with schizophrenia (ASZ), 16 adolescents with bipolar disorder with psychosis (ABP), and 14 typically developing individuals (TD) METHODS: The data were analysed using time-series techniques and dynamic causal modelling (DCM). OUTCOME MEASURES: MEG difference wave (deviant - standard) at primary auditory (~90ms), MMN (~180ms) and long latency (~300ms). RESULTS: The amplitude of difference wave showed specific patterns at all latencies. Most notably, it was significantly reduced ABP compared to both controls and ASZ at early latencies. In contrast, the amplitude was significantly reduced in ASZ compared to both controls and ABP. The DCM analysis showed differential connectivity patterns in all three groups. Most notably, inter-hemispheric connections were strongly dominated by the right side in ASZ only. CONCLUSIONS: Dissociable patterns of the primary auditory response and MMN response indicate possible developmentally sensitive, but separate biomarkers for schizophrenia and bipolar disorder

Neuroticism and conscientiousness respectively constrain and facilitate short-term plasticity within the working memory neural network

Individual differences in cognitive efficiency, particularly in relation to working memory (WM), have been associated both with personality dimensions that reflect enduring regularities in brain configuration, and with short-term neural plasticity, that reflects task-related changes in brain connectivity. To elucidate the relationship of these two divergent mechanisms, we tested the hypothesis that personality dimensions, which reflect enduring aspects of brain configuration, inform about the neurobiological framework within which short-term, task-related plasticity, as measured by effective connectivity, can be facilitated or constrained. As WM consistently engages the dorsolateral prefrontal (DLPFC), parietal (PAR), and anterior cingulate cortex (ACC), we specified a WM network model with bidirectional, ipsilateral, and contralateral connections between these regions from a functional magnetic resonance imaging dataset obtained from 40 healthy adults while performing the 3-back WM task. Task-related effective connectivity changes within this network were estimated using Dynamic Causal Modelling. Personality was evaluated along the major dimensions of Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Only two dimensions were relevant to task-dependent effective connectivity. Neuroticism and Conscientiousness respectively constrained and facilitated neuroplastic responses within the WM network. These results suggest individual differences in cognitive efficiency arise from the interplay between enduring and short-term plasticity in brain configuration

Effects of imperfections for Shor's factorization algorithm

We study effects of imperfections induced by residual couplings between qubits on the accuracy of Shor's algorithm using numerical simulations of realistic quantum computations with up to 30 qubits. The factoring of numbers up to N=943 show that the width of peaks, which frequencies allow to determine the factors, grow exponentially with the number of qubits. However, the algorithm remains operational up to a critical coupling strength $\epsilon_c$ which drops only polynomially with $\log_2 N$. The numerical dependence of $\epsilon_c$ on $\log_2 N$ is explained by analytical estimates that allows to obtain the scaling for functionality of Shor's algorithm on realistic quantum computers with a large number of qubits.Comment: 10 pages, 10 figures, 1 table. Added references and new data. Erratum added as appendix. 1 Figure and 1 Table added. Research is available at http://www.quantware.ups-tlse.fr