Center for Neural Informatics, Neural Structures, and Neural Plasticity

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    Older adults report moderately more detailed autobiographical memories
    (Frontiers Media, 2015-05-19) Gardner, Robert, S; Mainetti, Matteo; Ascoli, Giorgio, A
    Autobiographical memory (AM) is an essential component of the human mind. Although the amount and types of subjective detail (content) that compose AMs constitute important dimensions of recall, age-related changes in memory content are not well characterized. Previously, we introduced the Cue-Recalled Autobiographical Memory test (CRAM; see http://cramtest.info), an instrument that collects subjective reports of AM content, and applied it to college-aged subjects. CRAM elicits AMs using naturalistic word-cues. Subsequently, subjects date each cued AM to a life period and count the number of remembered details from specified categories (features), e.g., temporal detail, spatial detail, persons, objects, and emotions. The current work applies CRAM to a broad range of individuals (18–78 years old) to quantify the effects of age on AM content. Subject age showed a moderately positive effect on AM content: older compared with younger adults reported ∼16% more details (∼25 vs. ∼21 in typical AMs). This age-related increase in memory content was similarly observed for remote and recent AMs, although content declined with the age of the event among all subjects. In general, the distribution of details across features was largely consistent among younger and older adults. However, certain types of details, i.e., those related to objects and sequences of events, contributed more to the age effect on content. Altogether, this work identifies a moderate age-related feature-specific alteration in the way life events are subjectively recalled, among an otherwise stable retrieval profile.
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    Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment
    (BioMed Central, 2015-07-04) Gillette, Todd A.; Hosseini, Parsa; Ascoli, Giorgio A.
    Background The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees. Results The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons. Conclusions Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.
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    Topological characterization of neuronal arbor morphology via sequence representation. I. Motif analysis
    (BioMed Central, 2015-07-10) Gillette, Todd A.; Ascoli, Giorgio A.
    Background. The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns. Results. Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile. Conclusions. The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.