Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework

A large-scale, cross-sectional investigation into the efficacy of brain training

Brain training is a large and expanding industry, and yet there is a recurrent and ongoing debate concerning its scientific basis or evidence for efficacy. Much of evidence for the efficacy of brain training within this debate is from small-scale studies that do not assess the type of “brain training,” the specificity of transfer effects, or the length of training required to achieve a generalized effect. To explore these factors, we analyze cross-sectional data from two large Internet-cohort studies (total N = 60,222) to determine whether cognition differs at the population level for individuals who report that they brain train on different devices, and across different timeframes, with programs in common use circa 2010–2013. Examining scores for an assessment of working-memory, reasoning and verbal abilities shows no cognitive advantages for individuals who brain train. This contrasts unfavorably with significant advantages for individuals who regularly undertake other cognitive pursuits such as computer, board and card games. However, finer grained analyses reveal a more complex relationship between brain training and cognitive performance. Specifically, individuals who have just begun to brain train start from a low cognitive baseline compared to individuals who have never engaged in brain training, whereas those who have trained for a year or more have higher working-memory and verbal scores compared to those who have just started, thus suggesting an efficacy for brain training over an extended period of time. The advantages in global function, working memory, and verbal memory after several months of training are plausible and of clinically relevant scale. However, this relationship is not evident for reasoning performance or self-report measures of everyday function (e.g., employment status and problems with attention). These results accord with the view that although brain training programs can produce benefits, these might extend to tasks that are operationally similar to the training regime. Furthermore, the duration of training regime required for effective enhancement of cognitive performance is longer than that applied in most previous studies

Performance of strawberry varieties in New Hampshire, Bulletin, no. 368

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

The use of lumber by New Hampshire\u27s wood-using industries, Station Bulletin, no.474

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

Preharvest apple drop with special reference to McIntosh, Bulletin, no. 355

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

Pollination and fruit setting in the apple, Bulletin, no. 274

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

Mosquitoes of New Hampshire, Bulletin, no. 243

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

Autosomal recessive primary microcephaly: an analysis of locus heterogeneity and phenotypic variation

BACKGROUND AND OBJECTIVES: Locus heterogeneity is well established in autosomal recessive primary microcephaly (MCPH) and to date five loci have been mapped. However, the relative contributions of these loci have not been assessed and genotype-phenotype correlations have not been investigated. DESIGN: A study population of 56 consanguineous families resident in or originating from northern Pakistan was ascertained and assessed by the authors. A panel of microsatellite markers spanning each of the MCPH loci was designed, against which the families were genotyped. RESULTS: The head circumference of the 131 affected subjects ranged from 4 to 14 SD below the mean, but there was little intrafamilial variation among affecteds (± 1 SD). MCPH5 was the most prevalent, with 24/56 families consistent with linkage; 2/56 families were compatible with linkage to MCPH1, 10/56 to MCPH2, 2/56 to MCPH3, none to MCPH4, and 18/56 did not segregate with any of the loci. CONCLUSIONS: MCPH5 is the most common locus in this population. On clinical grounds alone, the phenotype of families linked to each MCPH locus could not be distinguished. We have also shown that further MCPH loci await discovery with a number of families as yet unlinked