Excellent agreement between genetic and hydrogen breath tests for lactase deficiency and the role of extended symptom assessment

Clinical manifestations of lactase (LCT) deficiency include intestinal and extra-intestinal symptoms. Lactose hydrogen breath test (H2-BT) is considered the gold standard to evaluate LCT deficiency (LD). Recently, the single-nucleotide polymorphism C/T(-13910) has been associated with LD. The objectives of the present study were to evaluate the agreement between genetic testing of LCT C/T(-13910) and lactose H2-BT, and the diagnostic value of extended symptom assessment. Of the 201 patients included in the study, 194 (139 females; mean age 38, range 17-79 years, and 55 males, mean age 38, range 18-68 years) patients with clinical suspicion of LD underwent a 3-4 h H2-BT and genetic testing for LCT C/T(-13910). Patients rated five intestinal and four extra-intestinal symptoms during the H2-BT and then at home for the following 48 h. Declaring H2-BT as the gold standard, the CC(-13910) genotype had a sensitivity of 97% and a specificity of 95% with a of 0.9 in diagnosing LCT deficiency. Patients with LD had more intense intestinal symptoms 4 h following the lactose challenge included in the H2-BT. We found no difference in the intensity of extra-intestinal symptoms between patients with and without LD. Symptom assessment yielded differences for intestinal symptoms abdominal pain, bloating, borborygmi and diarrhoea between 120 min and 4 h after oral lactose challenge. Extra-intestinal symptoms (dizziness, headache and myalgia) and extension of symptom assessment up to 48 h did not consistently show different results. In conclusion, genetic testing has an excellent agreement with the standard lactose H2-BT, and it may replace breath testing for the diagnosis of LD. Extended symptom scores and assessment of extra-intestinal symptoms have limited diagnostic value in the evaluation of LD.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Intraobserver and interobserver agreement for results of low-field magnetic resonance imaging in dogs with and without clinical signs of disk-associated wobbler syndrome

OBJECTIVE: To determine interobserver and intraobserver agreement for results of low-field magnetic resonance imaging (MRI) in dogs with and without disk-associated wobbler syndrome (DAWS). DESIGN: Validation study. ANIMALS: 21 dogs with and 23 dogs without clinical signs of DAWS. PROCEDURES: For each dog, MRI of the cervical vertebral column was performed. The MRI studies were presented in a randomized sequence to 4 board-certified radiologists blinded to clinical status. Observers assessed degree of disk degeneration, disk-associated and dorsal compression, alterations in intraspinal signal intensity (ISI), vertebral body abnormalities, and new bone formation and categorized each study as originating from a clinically affected or clinically normal dog. Interobserver agreement was calculated for 44 initial measurements for each observer. Intraobserver agreement was calculated for 11 replicate measurements for each observer. RESULTS: There was good interobserver agreement for ratings of disk degeneration and vertebral body abnormalities and moderate interobserver agreement for ratings of disk-associated compression, dorsal compression, alterations in ISI, new bone formation, and suspected clinical status. There was very good intraobserver agreement for ratings of disk degeneration, disk-associated compression, alterations in ISI, vertebral body abnormalities, and suspected clinical status. There was good intraobserver agreement for ratings of dorsal compression and new bone formation. Two of 21 clinically affected dogs were erroneously categorized as clinically normal, and 4 of 23 clinically normal dogs were erroneously categorized as clinically affected. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that variability exists among observers with regard to results of MRI in dogs with DAWS and that MRI could lead to false-positive and false-negative assessments.