Empirical research has shown that the amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC) are involved in fear conditioning. However, the functional contribution of each brain area and the nature of their interactions are not clearly understood. Here, we extend existing neural network models of the functional roles of the hippocampus in classical conditioning to include interactions with the amygdala and prefrontal cortex. We apply the model to fear conditioning, in which animals learn physiological (e.g. heart rate) and behavioral (e.g. freezing) responses to stimuli that have been paired with a highly aversive event (e.g. electrical shock). The key feature of our model is that learning of these conditioned responses in the central nucleus of the amygdala is modulated by two separate processes, one from basolateral amygdala and signaling a positive prediction error, and one from the vmPFC, via the intercalated cells of the amygdala, and signaling a negative prediction error. In addition, we propose that hippocampal input to both vmPFC and basolateral amygdala is essential for contextual modulation of fear acquisition and extinction. The model is sufficient to account for a body of data from various animal fear conditioning paradigms, including acquisition, extinction, reacquisition, and context specificity effects. Consistent with studies on lesioned animals, our model shows that damage to the vmPFC impairs extinction, while damage to the hippocampus impairs extinction in a different context (e.g., a different conditioning chamber from that used in initial training in animal experiments). We also discuss model limitations and predictions, including the effects of number of training trials on fear conditioning.
fear conditioning; computational model; hippocampus; amygdala; ventromedial prefrontal cortex; extinction
Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection.
In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata.
These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454).
Metagenomics; Phylogenetics; Forensics; Bayes factor; Microbial diversity; Community structure; Microbial ecology; Edge PCA; Phylogenetic diversity; Microbial evolution
Parkinson's disease; freezing of gait; response conflict; computational modeling
A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning.
Computational model; hippocampus; fear and eyeblink conditioning; interstimulus interval (ISI); hippocampal lesion (HL); trace conditioning; short vs. long delay conditioning; associative learning
The GABA transporter (GAT) group is one of the major subgroups in the solute career 6 (SLC6) family of transmembrane proteins. The GAT group, which has been well studied in mammals, has 6 known members, i.e., a taurine transporter (TAUT), four GABA transporters (GAT-1, -2, -3, - 4), and a creatine transporter (CT1), which have important roles in maintaining physiological homeostasis. However, the GAT group has not been extensively investigated in invertebrates; only TAUT has been reported in marine invertebrates such as bivalves and krills, and GAT-1 has been reported in several insect species and nematodes. Thus, it is unknown how transporters in the GAT group arose during the course of animal evolution. In this study, we cloned GAT-1 cDNAs from the deep-sea mussel, Bathymodiolus septemdierum, and the Antarctic krill, Euphausia superba, whose TAUT cDNA has already been cloned. To understand the evolutionary history of the GAT group, we conducted phylogenetic and synteny analyses on the GAT group transporters of vertebrates and invertebrates. Our findings suggest that transporters of the GAT group evolved through the following processes. First, GAT-1 and CT1 arose by tandem duplication of an ancestral transporter gene before the divergence of Deuterostomia and Protostomia; next, the TAUT gene arose and GAT-3 was formed by the tandem duplication of the TAUT gene; and finally, GAT-2 and GAT-4 evolved from a GAT-3 gene by chromosomal duplication in the ancestral vertebrates. Based on synteny and phylogenetic evidence, the present naming of the GAT group members does not accurately reflect the evolutionary relationships.
Emiliania huxleyi, a key player in the global carbon cycle is one of the best studied coccolithophores with respect to biogeochemical cycles, climatology, and host-virus interactions. Strains of E. huxleyi show phenotypic plasticity regarding growth behaviour, light-response, calcification, acidification, and virus susceptibility. This phenomenon is likely a consequence of genomic differences, or transcriptomic responses, to environmental conditions or threats such as viral infections. We used an E. huxleyi genome microarray based on the sequenced strain CCMP1516 (reference strain) to perform comparative genomic hybridizations (CGH) of 16 E. huxleyi strains of different geographic origin. We investigated the genomic diversity and plasticity and focused on the identification of genes related to virus susceptibility and coccolith production (calcification). Among the tested 31940 gene models a core genome of 14628 genes was identified by hybridization among 16 E. huxleyi strains. 224 probes were characterized as specific for the reference strain CCMP1516. Compared to the sequenced E. huxleyi strain CCMP1516 variation in gene content of up to 30 percent among strains was observed. Comparison of core and non-core transcripts sets in terms of annotated functions reveals a broad, almost equal functional coverage over all KOG-categories of both transcript sets within the whole annotated genome. Within the variable (non-core) genome we identified genes associated with virus susceptibility and calcification. Genes associated with virus susceptibility include a Bax inhibitor-1 protein, three LRR receptor-like protein kinases, and mitogen-activated protein kinase. Our list of transcripts associated with coccolith production will stimulate further research, e.g. by genetic manipulation. In particular, the V-type proton ATPase 16 kDa proteolipid subunit is proposed to be a plausible target gene for further calcification studies.
Dinoflagellates are important primary producers, crucial in marine food webs. Toxic strains, however, are the main causative agents of non-bacterial seafood poisoning, a major concern for public health worldwide. Despite their importance, taxonomic uncertainty within many genera of dinoflagellates is still high. The genus Coolia includes potentially harmful species and the diversity within the genus is just starting to become apparent.
In the current study, cultures were established from strains of Coolia spp. isolated from the central Great Barrier Reef (GBR). Cultures were identified based on thecal plate morphology and analyses of sequences (18S, ITS and 28S) from the nuclear rRNA operon. We report that the central GBR harbors a high diversity of Coolia species, including two species known to be capable of toxin production (C. tropicalis and C. malayensis), as well as the non-toxic C. canariensis. The strain of C. canariensis isolated from the GBR may in fact be a cryptic species, closely related but nevertheless phylogenetically distinct from the strain on which the holotype of C. canariensis was based. We also found evidence of the occurrence of a cryptic species morphologically very similar to both C. malayensis and C. monotis. The consequences of taxonomic confusion within the genus are discussed.
The central GBR region harbors a previously unreported high diversity of Coolia spp., including two species known to potentially produce toxins. The presence of a cryptic species of unknown toxicity highlights the importance of cryptic diversity within dinoflagellates.
Although holoplankton are ocean drifters and exhibit high dispersal potential, a number of studies on single species are finding highly divergent genetic clades. These cryptic species complexes are important to discover and describe, as identification of common marine species is fundamental to understanding ecosystem dynamics. Here we investigate the global diversity within Pleuromamma piseki and P. gracilis, two dominant members of the migratory zooplankton assemblage in subtropical and tropical waters worldwide. Using DNA sequence data from the mitochondrial gene cytochrome c oxidase subunit II (mtCOII) from 522 specimens collected across the Pacific, Atlantic and Indian Oceans, we discover twelve well-resolved genetically distinct clades in this species complex (Bayesian posterior probabilities >0.7; 6.3–17% genetic divergence between clades). The morphologically described species P. piseki and P. gracilis did not form monophyletic groups, rather they were distributed throughout the phylogeny and sometimes co-occurred within well-resolved clades: this result suggests that morphological characters currently used for taxonomic identification of P. gracilis and P. piseki may be inaccurate as indicators of species’ boundaries. Cryptic clades within the species complex ranged from being common to rare, and from cosmopolitan to highly restricted in distribution across the global ocean. These novel lineages appear to be ecologically divergent, with distinct biogeographic distributions across varied pelagic habitats. We hypothesize that these mtDNA lineages are distinct species and suggest that resolving their systematic status is important, given the ecological significance of the genus Pleuromamma in subtropical-tropical waters worldwide.
The pelagic brown alga Sargassum forms an oasis of biodiversity and productivity in an otherwise featureless ocean surface. The vast pool of oil resulting from the Deepwater Horizon oil spill came into contact with a large portion of the Gulf of Mexico’s floating Sargassum mats. Aerial surveys performed during and after the oil spill show compelling evidence of loss and subsequent recovery of Sargassum. Expanding on the trends observed in the aerial surveys, we conducted a series of mesocosm experiments to test the effect of oil and dispersants on the vertical position and weight of the Sargassum complex (Sargassum natans and S. fluitans), as well as on the dissolved oxygen concentrations surrounding the algae. Dispersant and dispersed-oil had significant effects on the vertical position of both species of Sargassum over a period of 72 hours. Similarly, dissolved oxygen concentrations were lowest in dispersant and dispersed-oil treatments, respectively. Cumulatively, our findings suggest three pathways for oil-spill related injury: (1) Sargassum accumulated oil on the surface exposing animals to high concentrations of contaminants; (2) application of dispersant sank Sargassum, thus removing the habitat and potentially transporting oil and dispersant vertically; and (3) low oxygen surrounded the habitat potentially stressing animals that reside in the alga. These pathways represent direct, sublethal, and indirect effects of oil and dispersant release that minimize the ecosystem services provided by floating Sargassum – the latter two effects are rarely considered in assessing impacts of oil spills or response procedures.
Ribosome inactivating proteins are enzymes that depurinate a specific adenine residue in the alpha-sarcin-ricin loop of the large ribosomal RNA, being ricin and Shiga toxins the most renowned examples. They are widely distributed in plants and their presence has also been confirmed in a few bacterial species. According to this taxonomic distribution, the current model about the origin and evolution of RIP genes postulates that an ancestral RIP domain was originated in flowering plants, and later acquired by some bacteria via horizontal gene transfer. Here, we unequivocally detected the presence of RIP genes in fungi and metazoa. These findings, along with sequence and phylogenetic analyses, led us to propose an alternative, more parsimonious, hypothesis about the origin and evolutionary history of the RIP domain, where several paralogous RIP genes were already present before the three domains of life evolved. This model is in agreement with the current idea of the Last Universal Common Ancestor (LUCA) as a complex, genetically redundant organism. Differential loss of paralogous genes in descendants of LUCA, rather than multiple horizontal gene transfer events, could account for the complex pattern of RIP genes across extant species, as it has been observed for other genes.
Post-traumatic stress disorder (PTSD) symptoms include behavioral avoidance which is acquired and tends to increase with time. This avoidance may represent a general learning bias; indeed, individuals with PTSD are often faster than controls on acquiring conditioned responses based on physiologically-aversive feedback. However, it is not clear whether this learning bias extends to cognitive feedback, or to learning from both reward and punishment. Here, male veterans with self-reported current, severe PTSD symptoms (PTSS group) or with few or no PTSD symptoms (control group) completed a probabilistic classification task that included both reward-based and punishment-based trials, where feedback could take the form of reward, punishment, or an ambiguous “no-feedback” outcome that could signal either successful avoidance of punishment or failure to obtain reward. The PTSS group outperformed the control group in total points obtained; the PTSS group specifically performed better than the control group on reward-based trials, with no difference on punishment-based trials. To better understand possible mechanisms underlying observed performance, we used a reinforcement learning model of the task, and applied maximum likelihood estimation techniques to derive estimated parameters describing individual participants’ behavior. Estimations of the reinforcement value of the no-feedback outcome were significantly greater in the control group than the PTSS group, suggesting that the control group was more likely to value this outcome as positively reinforcing (i.e., signaling successful avoidance of punishment). This is consistent with the control group’s generally poorer performance on reward trials, where reward feedback was to be obtained in preference to the no-feedback outcome. Differences in the interpretation of ambiguous feedback may contribute to the facilitated reinforcement learning often observed in PTSD patients, and may in turn provide new insight into how pathological behaviors are acquired and maintained in PTSD.
Traditionally Cocholodinium and Gymnodinium sensu lato clade are distinguished based on the cingulum turn number, which has been increasingly recognized to be inadequate for Gymnodiniales genus classification. This has been improved by the combination of the apical groove characteristics and molecular phylogeny, which has led to the erection of several new genera (Takayama, Akashiwo, Karenia, and Karlodinium). Taking the apical groove characteristics and molecular phylogeny combined approach, we reexamined the historically taxonomically uncertain species Cochlodinium geminatum that formed massive blooms in Pearl River Estuary, China, in recent years. Samples were collected from a bloom in 2011 for morphological, characteristic pigment, and molecular analyses. We found that the cingulum in this species wraps around the cell body about 1.2 turns on average but can appear under the light microscopy to be >1.5 turns after the cells have been preserved. The shape of its apical groove, however, was stably an open-ended anticlockwise loop of kidney bean shape, similar to that of Polykrikos. Furthermore, the molecular phylogenetic analysis using 18S rRNA-ITS-28S rRNA gene cistron we obtained in this study also consistently placed this species closest to Polykrikos within the Gymnodinium sensu stricto clade and set it far separated from the clade of Cochlodinium. These results suggest that this species should be transferred to Polykrikos as Polykrikos geminatum. Our results reiterate the need to use the combination of apical groove morphology and molecular phylogeny for the classification of species within the genus of Cochlodinium and other Gymnodiniales lineages.
In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.
Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome.
Damage to the hippocampal and frontostriatal systems can occur across the adult life span. As these 2systems are involved in learning processes, mild impairments of learning and generalization might be observed even in healthy aging. In this study, we examined both learning and generalization performance in 3 groups of older adults: young-older (ages 45–60), middle-older (ages 61–75), and oldest-older (ages 76–90).We used a simple computerized concurrent discrimination task in which the learning phase has demonstrated sensitivity to frontostriatal dysfunction, and the generalization phase to hippocampal damage. We found that age significantly affected initial learning performance, but generalization was spared in all but the oldest group, with some individuals still generalizing very well. This finding suggests that (a) learning abilities are affected in healthy aging (consistent with earlier reports of frontostriatal dysfunction in healthy aging) and (b) generalization deficit does not necessarily occur in early older age. We hypothesize that generalization deficits in some in the oldest group may be related to hippocampal pathology. Our data shed light on possible neural system dysfunction in healthy aging and Alzheimer disease.
learning; generalization; basal ganglia; hippocampus; Alzheimer disease
Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: firstname.lastname@example.org.
One barrier to interpreting past studies of cognition and major depressive disorder (MDD) has been the failure in many studies to adequately dissociate the effects of MDD from the potential cognitive side effects of selective serotonin reuptake inhibitors (SSRIs) use. To better understand how remediation of depressive symptoms affects cognitive function in MDD, we evaluated three groups of subjects: medication-naïve patients with MDD, medicated patients with MDD receiving the SSRI paroxetine, and healthy control (HC) subjects. All were administered a category-learning task that allows for dissociation between learning from positive feedback (reward) vs. learning from negative feedback (punishment). Healthy subjects learned significantly better from positive feedback than medication-naïve and medicated MDD groups, whose learning accuracy did not differ significantly. In contrast, medicated patients with MDD learned significantly less from negative feedback than medication-naïve patients with MDD and healthy subjects, whose learning accuracy was comparable. A comparison of subject’s relative sensitivity to positive vs. negative feedback showed that both the medicated MDD and HC groups conform to Kahneman and Tversky’s (1979) Prospect Theory, which expects losses (negative feedback) to loom psychologically slightly larger than gains (positive feedback). However, medicated MDD and HC profiles are not similar, which indicates that the state of medicated MDD is not “normal” when compared to HC, but rather balanced with less learning from both positive and negative feedback. On the other hand, medication-naïve patients with MDD violate Prospect Theory by having significantly exaggerated learning from negative feedback. This suggests that SSRI antidepressants impair learning from negative feedback, while having negligible effect on learning from positive feedback. Overall, these findings shed light on the importance of dissociating the cognitive consequences of MDD from those of SSRI treatment, and from cognitive evaluation of MDD subjects in a medication-naïve state before the administration of antidepressants. Future research is needed to correlate the mood-elevating effects and the cognitive balance between reward- and punishment-based learning related to SSRIs.
major depressive disorder; selective serotonin reuptake inhibitor; basal ganglia; reward; punishment
Freezing of gait (FOG) is a disabling symptom of advanced Parkinson's disease (PD) that leads to an increased risk of falls and nursing home placement. Interestingly, multiple lines of evidence suggest that the manifestation of FOG is related to specific deficits in cognition, such as set shifting and the ability to process conflict-related signals. These findings are consistent with the specific patterns of abnormal cortical processing seen during functional neuroimaging experiments of FOG, implicating increased neural activation within cortical structures underlying cognition, such as the Cognitive Control Network. In addition, these studies show that freezing episodes are associated with abnormalities in the BOLD response within key structures of the basal ganglia, such as the striatum and the subthalamic nucleus. In this article, we discuss the implications of these findings on current models of freezing behavior and propose an updated model of basal ganglia impairment during FOG episodes that integrates the neural substrates of freezing from the cortex and the basal ganglia to the cognitive dysfunctions inherent in the condition.
Parkinson's disease; freezing of gait; functional decoupling; subthalamic nucleus; pedunculopontine tegmental nucleus
PTSD; hippocampus; amygdala; neurogenetics; cognitive behavioral therapy; computational modeling
Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.
basal ganglia; computational cognitive neuroscience; cognitive function; motor function; Parkinson’s disease
Discrimination learning deficits in Parkinson's disease (PD) have been well-established. Using both behavioral patient studies and computational approaches, these deficits have typically been attributed to dopamine imbalance across the basal ganglia. However, this explanation of impaired learning in PD does not account for the possible contribution of other pathological changes that occur in the disease process, importantly including gray matter loss. To address this gap in the literature, the current study explored the relationship between fronto-striatal gray matter atrophy and learning in PD. We employed a discrimination learning task and computational modeling in order to assess learning rates in non-demented PD patients. Behaviorally, we confirmed that learning rates were reduced in patients relative to controls. Furthermore, voxel-based morphometry imaging analysis demonstrated that this learning impairment was directly related to gray matter loss in discrete fronto-striatal regions (specifically, the ventromedial prefrontal cortex, inferior frontal gyrus and nucleus accumbens). These findings suggest that dopaminergic imbalance may not be the sole determinant of discrimination learning deficits in PD, and highlight the importance of factoring in the broader pathological changes when constructing models of learning in PD.
Parkinson's disease; discrimination learning; goal-directed learning; computational modeling; voxel-based morphometry; fronto-striatal
Parkinson’s disease (PD) is a neurological disorder, associated with rigidity, bradykinesia, and resting tremor, among other motor symptoms. In addition, patients with PD also show cognitive and psychiatric dysfunction, including dementia, mild cognitive impairment (MCI), depression, hallucinations, among others. Interestingly, the occurrence of these symptoms—motor, cognitive, and psychiatric—vary among individuals, such that a subgroup of PD patients might show some of the symptoms, but another subgroup does not. This has prompted neurologists and scientists to subtype PD patients depending on the severity of symptoms they show. Neural studies have also mapped different motor, cognitive, and psychiatric symptoms in PD to different brain networks. In this review, we discuss the neural and behavioral substrates of most common subtypes of PD patients, that are related to the occurrence of: (a) resting tremor (vs. nontremor-dominant); (b) MCI; (c) dementia; (d) impulse control disorders (ICD); (e) depression; and/or (f) hallucinations. We end by discussing the relationship among subtypes of PD subgroups, and the relationship among motor, cognitive, psychiatric factors in PD.
Parkinson’s disease; tremor; dementia; mild cognitive impairment; hallucinations; depression; impulse control disorders
We present a computational model of altered gait velocity patterns in Parkinson's Disease (PD) patients. PD gait is characterized by short shuffling steps, reduced walking speed, increased double support time and sometimes increased cadence. The most debilitating symptom of PD gait is the context dependent cessation in gait known as freezing of gait (FOG). Cowie et al. (2010) and Almeida and Lebold (2010) investigated FOG as the changes in velocity profiles of PD gait, as patients walked through a doorway with variable width. The former reported a sharp dip in velocity, a short distance from the doorway that was greater for narrower doorways. They compared the gait performance in PD freezers at ON and OFF dopaminergic medication. In keeping with this finding, the latter also reported the same for ON medicated PD freezers and non-freezers. In the current study, we sought to simulate these gait changes using a computational model of Basal Ganglia based on Reinforcement Learning, coupled with a spinal rhythm mimicking central pattern generator (CPG) model. In the model, a simulated agent was trained to learn a value profile over a corridor leading to the doorway by repeatedly attempting to pass through the doorway. Temporal difference error in value, associated with dopamine signal, was appropriately constrained in order to reflect the dopamine-deficient conditions of PD. Simulated gait under PD conditions exhibited a sharp dip in velocity close to the doorway, with PD OFF freezers showing the largest decrease in velocity compared to PD ON freezers and controls. PD ON and PD OFF freezers both showed sensitivity to the doorway width, with narrow door producing the least velocity/ stride length. Step length variations were also captured with PD freezers producing smaller steps and larger step-variability than PD non-freezers and controls. In addition this model is the first to explain the non-dopamine dependence for FOG giving rise to several other possibilities for its etiology.
gait; freezing of gait; doorway; basal ganglia; reinforcement learning
Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between RL models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both RL and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.
reinforcement learning; basal ganglia; dopamine; interval timing; Parkinson's disease
Molecular tools may greatly improve our understanding of pathogen evolution and epidemiology but technical constraints have hindered the development of genetic resources for parasites compared to free-living organisms. This study aims at developing molecular tools for Podosphaera plantaginis, an obligate fungal pathogen of Plantago lanceolata. This interaction has been intensively studied in the Åland archipelago of Finland with epidemiological data collected from over 4,000 host populations annually since year 2001.
A cDNA library of a pooled sample of fungal conidia was sequenced on the 454 GS-FLX platform. Over 549,411 reads were obtained and annotated into 45,245 contigs. Annotation data was acquired for 65.2% of the assembled sequences. The transcriptome assembly was screened for SNP loci, as well as for functionally important genes (mating-type genes and potential effector proteins). A genotyping assay of 27 SNP loci was designed and tested on 380 infected leaf samples from 80 populations within the Åland archipelago. With this panel we identified 85 multilocus genotypes (MLG) with uneven frequencies across the pathogen metapopulation. Approximately half of the sampled populations contain polymorphism. Our genotyping protocol revealed mixed-genotype infection within a single host leaf to be common. Mixed infection has been proposed as one of the main drivers of pathogen evolution, and hence may be an important process in this pathosystem.
The developed SNP panel offers exciting research perspectives for future studies in this well-characterized pathosystem. Also, the transcriptome provides an invaluable novel genomic resource for powdery mildews, which cause significant yield losses on commercially important crops annually. Furthermore, the features that render genetic studies in this system a challenge are shared with the majority of obligate parasitic species, and hence our results provide methodological insights from SNP calling to field sampling protocols for a wide range of biological systems.