Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD. Impact statement In this work, we review the default mode network in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), as well as comorbid ASD+ADHD literature. Such a review has not been constructed in the field of cognitive neuroscience at this time, and it would greatly aid other behavioral and cognitive neuroscientists in identifying gaps in the field. In addition, the need to consider disorders to be on a continuum, as suggested by the Research Domain Criteria (RDoC), is important while identifying abnormal patterns in resting-state functional connectivity. This timely review will impact the field in a meaningful way, such that more research on the overlaps between ASD and ADHD is conducted along a spectrum.

Primary and secondary structural data from the internal transcribed spacer two (ITS2) have been used extensively for diversity studies of many different eukaryotic organisms, including the green algae. Ease of amplification is due, at least in part, to the fact that ITS2 is part of the tandemly-repeated rRNA array. The potential confounding influence of intragenomic variability has yet to be addressed except in a few organisms. Moreover, few of the assessments of intragenomic variation have taken advantage of the deep sequencing capacity of sequence-by-synthesis protocols. We present results from our adaptation of the 16S Metagenomics Sequencing Library Preparation/Illumina protocol for deep sequencing of the ITS2 genes in selected isolates of the green algal genus, Haematococcus. Deep sequencing yielded from just under 20,000 to more than 500,000 merged reads, outpacing results from recent pyrosequencing efforts. Furthermore, a conservative evaluation of these data revealed a range of three to six ITS2 sequence haplotypes (defined as unique sets of nucleotide polymorphisms) across the taxon sampling. The frequency of the dominant haplotype ranged from 0.35 to 0.98. In all but two cases, the haplotype with the greatest frequency corresponded to a sequence obtained by the Sanger method using PCR templates. Our data also show that results from the sequencing-by-synthesis approach are reproducible. In addition to advancing our understanding of ribosomal RNA variation, the results of this investigation will allow us to begin testing hypotheses regarding the maintenance of homogeneity across multi-copy genes.

Difficulty remembering faces and names is a common struggle for many people and gets more difficult as we age. Subtle changes in appearance from day to day, common facial characteristics across individuals, and overlap of names may contribute to the difficulty of learning face-name associations. Computational models suggest the hippocampus plays a key role in reducing interference across experiences with overlapping information by performing pattern separation, which enables us to encode similar experiences as distinct from one another. Thus, given the nature of overlapping features within face-name associative memory, hippocampal pattern separation may be an important underlying mechanism supporting this type of memory. Furthermore, cross-species approaches find that aging is associated with deficits in hippocampal pattern separation. Mnemonic discrimination tasks have been designed to tax hippocampal pattern separation and provide a more sensitive measure of age-related cognitive decline compared to traditional memory tasks. However, current face-name associative memory tasks do not explicitly tax hippocampal pattern separation and often lack naturalistic facial features (e.g., hair, accessories, similarity of features, emotional expressions). Here, we developed a face-name mnemonic discrimination task where we varied face stimuli by similarity, race, sex, and emotional expression as well as the similarity of name stimuli. We tested a sample of healthy young and older adults on this task and found that both age groups showed worsening performance as face-name interference increased. Overall, older adults struggled to remember faces and face-name pairs more than young adults. However, while young adults remembered emotional faces better than neutral faces, older adults selectively remembered positive faces. These results suggest that the inclusion of stimuli that more closely mimic face-name memory in real life may provide a more sensitive measure of age-related cognitive decline compared to standard face-name associative memory tasks.

Retirement is a period of significant change, as older adults transition from a lifetime of work to unstructured leisure time. This sudden shift in activity may have drastic consequences on cognition and disease risk. Retirement has been associated with declines in memory beyond typical age-related memory decline and may continue to deteriorate steadily the longer older adults remain retired. Mnemonic discrimination tasks have been developed that provide a more sensitive measure of age-related memory decline compared to traditional memory tasks by taxing hippocampal pattern separation, or the process of reducing interference across similar experiences. While older adults are more susceptible to interference in memory, positive experiences tend to be better remembered. The socioemotional selectivity theory suggests that awareness of a limited remaining lifespan leads older adults to prioritize positive experiences to facilitate emotional well-being. Therefore, retired older adults may be more susceptible to the positivity effect in aging. Here, we utilized an emotional mnemonic discrimination task to examine how retirement influences emotional memory. We found that retired older adults show selective impairments for mnemonic discrimination relative to standard memory measures, and a larger positivity effect in memory compared to their age-matched working peers. However, subjective memory complaints, job stress, and depressive symptoms impacted these relationships depending on retirement status. These findings are in line with the socioemotional selectivity theory, in which retirement may be associated with a prioritization of positive experiences; however, it is unclear whether this effect is compensatory or perhaps an indicator of age-related memory dysfunction.

Difficulty remembering faces and names is a common struggle for many people and gets more difficult as we age. Subtle changes in appearance from day to day, common facial characteristics across individuals, and overlap of names may contribute to the difficulty of learning face-name associations. Computational models suggest the hippocampus plays a key role in reducing interference across experiences with overlapping information by performing pattern separation, which enables us to encode similar experiences as distinct from one another. Thus, given the nature of overlapping features within face-name associative memory, hippocampal pattern separation may be an important underlying mechanism supporting this type of memory. Furthermore, cross-species approaches find that aging is associated with deficits in hippocampal pattern separation. Mnemonic discrimination tasks have been designed to tax hippocampal pattern separation and provide a more sensitive measure of age-related cognitive decline compared to traditional memory tasks. However, current face-name associative memory tasks do not explicitly tax hippocampal pattern separation and often lack naturalistic facial features (e.g., hair, accessories, similarity of features, emotional expressions). Here, we developed a face-name mnemonic discrimination task where we varied face stimuli by similarity, race, sex, and emotional expression as well as the similarity of name stimuli. We tested a sample of healthy young and older adults on this task and found that both age groups showed worsening performance as face-name interference increased. Overall, older adults struggled to remember faces and face-name pairs more than young adults. However, while young adults remembered emotional faces better than neutral faces, older adults selectively remembered positive faces. These results suggest that the inclusion of stimuli that more closely mimic face-name memory in real life may provide a more sensitive measure of age-related cognitive decline compared to standard face-name associative memory tasks.