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1.  Pattern Association and Consolidation Emerges from Connectivity Properties between Cortex and Hippocampus 
PLoS ONE  2014;9(1):e85016.
The basic structure of the cortico-hippocampal system is highly conserved across mammalian species. Comparatively few hippocampal neurons can represent and address a multitude of cortical patterns, establish associations between cortical patterns and consolidate these associations in the cortex. In this study, we investigate how elementary anatomical properties in the cortex-hippocampus loop along with synaptic plasticity contribute to these functions. Specifically, we focus on the high degree of connectivity between cortex and hippocampus leading to converging and diverging forward and backward projections and heterogenous synaptic transmission delays that result from the detached location of the hippocampus and its multiple loops. We found that in a model incorporating these concepts, each cortical pattern can evoke a unique spatio-temporal spiking pattern in hippocampal neurons. This hippocampal response facilitates a reliable disambiguation of learned associations and a bridging of a time interval larger than the time window of spike-timing dependent plasticity in the cortex. Moreover, we found that repeated retrieval of a stored association leads to a compression of the interval between cue presentation and retrieval of the associated pattern from the cortex. Neither a high degree of connectivity nor heterogenous synaptic delays alone is sufficient for this behavior. We conclude that basic anatomical properties between cortex and hippocampus implement mechanisms for representing and consolidating temporal information. Since our model reveals the observed functions for a range of parameters, we suggest that these functions are robust to evolutionary changes consistent with the preserved function of the hippocampal loop across different species.
PMCID: PMC3880336  PMID: 24404200
2.  Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections 
Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.
PMCID: PMC4164034  PMID: 25309338
3d model; functional morphology; hippocampal formation; Blender; NEST; connection patterns; conduction latencies; brain anatomy
3.  Local Synchronization of Resting-State Dynamics Encodes Gray’s Trait Anxiety 
PLoS ONE  2013;8(3):e58336.
The Behavioral Inhibition System (BIS) as defined within the Reinforcement Sensitivity Theory (RST) modulates reactions to stimuli indicating aversive events. Gray’s trait Anxiety determines the extent to which stimuli activate the BIS. While studies have identified the amygdala-septo-hippocampal circuit as the key-neural substrate of this system in recent years and measures of resting-state dynamics such as randomness and local synchronization of spontaneous BOLD fluctuations have recently been linked to personality traits, the relation between resting-state dynamics and the BIS remains unexplored. In the present study, we thus examined the local synchronization of spontaneous fMRI BOLD fluctuations as measured by Regional Homogeneity (ReHo) in the hippocampus and the amygdala in twenty-seven healthy subjects. Correlation analyses showed that Gray’s trait Anxiety was significantly associated with mean ReHo in both the amygdala and the hippocampus. Specifically, Gray’s trait Anxiety explained 23% and 17% of resting-state ReHo variance in the left amygdala and the left hippocampus, respectively. In summary, we found individual differences in Gray’s trait Anxiety to be associated with ReHo in areas previously associated with BIS functioning. Specifically, higher ReHo in resting-state neural dynamics corresponded to lower sensitivity to punishment scores both in the amygdala and the hippocampus. These findings corroborate and extend recent findings relating resting-state dynamics and personality while providing first evidence linking properties of resting-state fluctuations to Gray’s BIS.
PMCID: PMC3592924  PMID: 23520499
4.  Functional Connectivity Analyses in Imaging Genetics: Considerations on Methods and Data Interpretation 
PLoS ONE  2011;6(12):e26354.
Functional magnetic resonance imaging (fMRI) can be combined with genotype assessment to identify brain systems that mediate genetic vulnerability to mental disorders (“imaging genetics”). A data analysis approach that is widely applied is “functional connectivity”. In this approach, the temporal correlation between the fMRI signal from a pre-defined brain region (the so-called “seed point”) and other brain voxels is determined. In this technical note, we show how the choice of freely selectable data analysis parameters strongly influences the assessment of the genetic modulation of connectivity features. In our data analysis we exemplarily focus on three methodological parameters: (i) seed voxel selection, (ii) noise reduction algorithms, and (iii) use of additional second level covariates. Our results show that even small variations in the implementation of a functional connectivity analysis can have an impact on the connectivity pattern that is as strong as the potential modulation by genetic allele variants. Some effects of genetic variation can only be found for one specific implementation of the connectivity analysis. A reoccurring difficulty in the field of psychiatric genetics is the non-replication of initially promising findings, partly caused by the small effects of single genes. The replication of imaging genetic results is therefore crucial for the long-term assessment of genetic effects on neural connectivity parameters. For a meaningful comparison of imaging genetics studies however, it is therefore necessary to provide more details on specific methodological parameters (e.g., seed voxel distribution) and to give information how robust effects are across the choice of methodological parameters.
PMCID: PMC3248388  PMID: 22220190
5.  Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers 
BioData Mining  2011;4:26.
Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.
We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.
Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.
PMCID: PMC3248369  PMID: 22082002
6.  Insights into the classification of small GTPases 
In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.
PMCID: PMC3170009  PMID: 21918623
cancer; machine learning; classification; Random Forests; proteins
7.  Impact of Working Memory Load on fMRI Resting State Pattern in Subsequent Resting Phases 
PLoS ONE  2009;4(9):e7198.
The default-mode network (DMN) is a functional network with increasing relevance for psychiatric research, characterized by increased activation at rest and decreased activation during task performance. The degree of DMN deactivation during a cognitively demanding task depends on its difficulty. However, the relation of hemodynamic responses in the resting phase after a preceding cognitive challenge remains relatively unexplored. We test the hypothesis that the degree of activation of the DMN following cognitive challenge is influenced by the cognitive load of a preceding working-memory task.
Methodology/Principal Findings
Twenty-five healthy subjects were investigated with functional MRI at 3 Tesla while performing a working-memory task with embedded short resting phases. Data were decomposed into statistically independent spatio-temporal components using Tensor Independent Component Analysis (TICA). The DMN was selected using a template-matching procedure. The spatial map contained rest-related activations in the medial frontal cortex, ventral anterior and posterior cingulate cortex. The time course of the DMN revealed increased activation at rest after 1-back and 2-back blocks compared to the activation after a 0-back block.
We present evidence that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task. This might be interpreted as a functional correlate of self-evaluation and reflection of the preceding task or as relocation of cerebral resources representing recovery from high cognitive demands. This finding is highly relevant for neuroimaging studies which include resting phases in cognitive tasks as stable baseline conditions. Further studies investigating the DMN should take possible interactions of tasks and subsequent resting phases into account.
PMCID: PMC2745698  PMID: 19779619
8.  DNA watermarks in non-coding regulatory sequences 
BMC Research Notes  2009;2:125.
DNA watermarks can be applied to identify the unauthorized use of genetically modified organisms. It has been shown that coding regions can be used to encrypt information into living organisms by using the DNA-Crypt algorithm. Yet, if the sequence of interest presents a non-coding DNA sequence, either the function of a resulting functional RNA molecule or a regulatory sequence, such as a promoter, could be affected. For our studies we used the small cytoplasmic RNA 1 in yeast and the lac promoter region of Escherichia coli.
The lac promoter was deactivated by the integrated watermark. In addition, the RNA molecules displayed altered configurations after introducing a watermark, but surprisingly were functionally intact, which has been verified by analyzing the growth characteristics of both wild type and watermarked scR1 transformed yeast cells. In a third approach we introduced a second overlapping watermark into the lac promoter, which did not affect the promoter activity.
Even though the watermarked RNA and one of the watermarked promoters did not show any significant differences compared to the wild type RNA and wild type promoter region, respectively, it cannot be generalized that other RNA molecules or regulatory sequences behave accordingly. Therefore, we do not recommend integrating watermark sequences into regulatory regions.
PMCID: PMC2713970  PMID: 19583865

Results 1-8 (8)