To better understand how medication status and task demands affect cognition in Major Depressive Disorder (MDD), we evaluated medication-naïve patients with MDD, medicated patients with MDD receiving the Selective Serotonin Reuptake Inhibitors (SSRI) paroxetine, and healthy controls. All three groups were administered a computer-based cognitive task with two phases, an initial phase in which a sequence is learned through reward-based feedback (which our prior studies suggest is striatal-dependent), followed by a generalization phase that involves a change in the context where learned rules are to be applied (which our prior studies suggest is hippocampal-region dependent). Medication-naïve MDD patients were slow to learn the initial sequence but were normal on subsequent generalization of that learning. In contrast, medicated patients learned the initial sequence normally, but were impaired at the generalization phase. We argue that these data suggest (i) an MDD-related impairment in striatal-dependent sequence learning which can be remediated by SSRIs and (ii) an SSRI-induced impairment in hippocampal-dependent generalization of past learning to novel contexts, not otherwise seen in the medication-naïve MDD group. Thus, SSRIs might have a beneficial effect on striatal function required for sequence learning, but a detrimental effect on the hippocampus and other medial temporal lobe structures critical for generalization.
Major Depressive Disorder; Selective Serotonin Reuptake Inhibitor (SSRI); hippocampus; basal ganglia; reward; punishment; sequence learning; context-shift; generalization
Metagenomics-based functional profiling analysis is an effective means of gaining deeper insight into the composition of marine microbial populations and developing a better understanding of the interplay between the functional genome content of microbial communities and abiotic factors. Here we present a comprehensive analysis of 24 datasets covering surface and depth-related environments at 11 sites around the world's oceans. The complete datasets comprises approximately 12 million sequences, totaling 5,358 Mb. Based on profiling patterns of Clusters of Orthologous Groups (COGs) of proteins, a core set of reference photic and aphotic depth-related COGs, and a collection of COGs that are associated with extreme oxygen limitation were defined. Their inferred functions were utilized as indicators to characterize the distribution of light- and oxygen-related biological activities in marine environments. The results reveal that, while light level in the water column is a major determinant of phenotypic adaptation in marine microorganisms, oxygen concentration in the aphotic zone has a significant impact only in extremely hypoxic waters. Phylogenetic profiling of the reference photic/aphotic gene sets revealed a greater variety of source organisms in the aphotic zone, although the majority of individual photic and aphotic depth-related COGs are assigned to the same taxa across the different sites. This increase in phylogenetic and functional diversity of the core aphotic related COGs most probably reflects selection for the utilization of a broad range of alternate energy sources in the absence of light.
Behavioral inhibition (BI) is a temperamental tendency to avoid or withdraw from novel social and nonsocial situations and has been shown to predispose individuals to anxiety disorders. However, adequate means to assess individual differences in avoidance learning in humans are presently limited. Here, we tested whether individuals with high self-reported BI show faster associative learning on a purely cognitive task, and whether such inhibited individuals are more prone to avoid aversive outcomes. In Experiment 1, we tested 74 healthy undergraduate students (mean age 19.5 years; 55.4% female) on a computer-based probabilistic classification task, where participants were asked to classify four distinct visual stimuli into two categories. Two stimuli were associated with reward (point gain) and two were associated with punishment (point loss). In Experiment 2, 79 participants from the same population (mean age 19.8 years; 62% female) were tested on a novel modification of the same task, where they also had the option to opt out of responding on each trial and thus, avoid any chance of being punished (or rewarded) on that trial. Results show that inhibited participants demonstrated better associative learning in Experiment 1, while exhibiting a greater tendency to opt out in Experiment 2 (repeated-measures ANOVAs, main effects of BI, both p<0.05). These results suggest that the facilitated classically-conditioned learning previously observed in inhibited individuals can be extended to a cognitive task, and also highlight a specific preference in inhibited individuals for withdrawal (“opting-out”) as a response strategy, when multiple strategies are available to avoid punishment.
Decision making; avoidance; PTSD (posttraumatic stress disorder); behavioral inhibition; associative learning; anxiety disorders
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
Parkinson's disease; freezing of gait; response conflict; computational modeling
Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.
serotonin; dopamine; basal ganglia; Reinforcement Learning; Risk; Reward; Punishment; Decision Making
Background: Prior studies report that monoamine oxidases inhibitors (MAO-I) when used as an adjunct to levodopa ameliorate motor symptoms in Parkinson’s disease (PD), but this was not tested in relation to cognitive or psychiatric measures.
Objective: Here, we tested the effects of MAO-I as an adjunct to levodopa, in comparison to levodopa or dopamine (DA) agonists alone, on various cognitive, affective and quality of life measures.
Methods: We studied three groups of subjects: healthy controls, PD patients on combined levodopa and MAO-I, and PD patients on levodopa or DA agonists only.
Results: We found that compared to monotherapy, combined MAO-I and levodopa seemed to improve cognition, including probabilistic learning, working memory and executive functions. There were no differences between the different medication regimes on deterministic learning, attention or memory recall. It was also found that MAO-I as an adjunct to levodopa improves affective measures such as depression, apathy, anxiety and quality of life. Interestingly, this enhancing effect of combined levodopa and MAO-I was more pronounced in PD patients with severe akinesia, compared to patients with severe tremor.
Conclusion: Our data are in agreement with (a) the Continuous Dopaminergic Stimulation (CDS) theory which states that continuous stimulation of the basal ganglia enhances motor, psychiatric and cognitive functions in PD patients; and/or (b) findings that MAO-I increase the bioavailability of monoamines that have beneficial effects on motor and behavioral dysfunction in PD.
Parkinson’s disease; MAO inhibitors; cognition; working memory; depression; anxiety; quality of life; learning
basal ganglia; dopamine; Parkinson's disease (PD); computational modeling; animal studies; human imaging studies; deep brain stimulation
Although homocysteine (Hcy) has been widely implicated in the etiology of various physical health impairments, especially cardiovascular diseases, overwhelming evidence indicates that Hcy is also involved in the pathophysiology of schizophrenia and affective disorders. There are several mechanisms linking Hcy to biological underpinnings of psychiatric disorders. It has been found that Hcy interacts with NMDA receptors, initiates oxidative stress, induces apoptosis, triggers mitochondrial dysfunction and leads to vascular damage. Elevated Hcy levels might also contribute to cognitive impairment that is widely observed among patients with affective disorders and schizophrenia. Supplementation of vitamins B and folic acid has been proved to be effective in lowering Hcy levels. There are also studies showing that this supplementation strategy might be beneficial for schizophrenia patients with respect to alleviating negative symptoms. However, there are no studies addressing the influence of add-on therapies with folate and vitamins B on cognitive performance of patients with schizophrenia and affective disorders. In this article, we provide an overview of Hcy metabolism in psychiatric disorders focusing on cognitive correlates and indicating future directions and perspectives.
homocysteine; depression; bipolar disorder; schizophrenia; hyperhomocysteinemia; cognition; brain substrates
aging; motor processes; cognitive processes; dopamine; acetylcholine; basal ganglia; hippocampus; prefrontal cortex
Comparisons of cognitive impairments between schizophrenia (SZ) and bipolar disorder (BPD) have produced mixed results. We applied different working memory (WM) measures (Digit Span Forward and Backward, Short-delay and Long-delay CPT-AX, N-back) to patients with SZ (n = 23), psychotic BPD (n = 19) and non-psychotic BPD (n = 24), as well as to healthy controls (HC) (n = 18) in order to compare the level of WM impairments across the groups. With respect to the less demanding WM measures (Digit Span Forward and Backward, Short-delay CPT-AX), there were no between group differences in cognitive performance; however, with respect to the more demanding WM measures (Long-delay CPT-AX, N-back), we observed that the groups with psychosis (SZ, psychotic BPD) did not differ from one another, but performed poorer than the group without a history of psychosis (non-psychotic BPD). A history of psychotic symptoms may influence cognitive performance with respect to WM delay and load effects as measured by Long-delay CPT-AX and N-back tests, respectively. We observed a positive correlation of WM performance with antipsychotic treatment and a negative correlation with depressive symptoms in BPD and with negative symptoms in SZ subgroup. Our study suggests that WM dysfunctions are more closely related to a history of psychosis than to the diagnostic categories of SZ and BPD described by psychiatric classification systems.
schizophrenia; psychotic vs. non-psychotic bipolar disorder; working memory; history of psychosis
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
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.
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
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
Studies of photosynthetic eukaryotes have revealed that the evolution of plastids from cyanobacteria involved the recruitment of non-cyanobacterial proteins. Our phylogenetic survey of >100 Arabidopsis nuclear-encoded plastid enzymes involved in amino acid biosynthesis identified only 21 unambiguous cyanobacterial-derived proteins. Some of the several non-cyanobacterial plastid enzymes have a shared phylogenetic origin in the three Plantae lineages. We hypothesize that during the evolution of plastids some enzymes encoded in the host nuclear genome were mistargeted into the plastid. Then, the activity of those foreign enzymes was sustained by both the plastid metabolites and interactions with the native cyanobacterial enzymes. Some of the novel enzymatic activities were favored by selective compartmentation of additional complementary enzymes. The mosaic phylogenetic composition of the plastid amino acid biosynthetic pathways and the reduced number of plastid-encoded proteins of non-cyanobacterial origin suggest that enzyme recruitment underlies the recompartmentation of metabolic routes during the evolution of plastids.
Previous studies have shown that high total homocysteine levels are associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). In this study, we test the relationship between cognitive function and total homocysteine levels in healthy subjects (Global Dementia Rating, CDR = 0) and individuals with MCI (CDR = 0.5). We have used a cognitive task that tests learning and generalization of rules, processes that have been previously shown to rely on the integrity of the striatal and hippocampal regions, respectively. We found that total homocysteine levels are higher in MCI individuals than in healthy controls. Unlike what we expected, we found no difference between MCI subjects and healthy controls in learning and generalization. We conducted further analysis after diving MCI subjects in two groups, depending on their Global Deterioration Scale (GDS) scores: individuals with very mild cognitive decline (vMCD, GDS = 2) and mild cognitive decline (MCD, GDS = 3). There was no difference among the two MCI and healthy control groups in learning performance. However, we found that individuals with MCD make more generalization errors than healthy controls and individuals with vMCD. We found no difference in the number of generalization errors between healthy controls and MCI individuals with vMCD. In addition, interestingly, we found that total homocysteine levels correlate positively with generalization errors, but not with learning errors. Our results are in agreement with prior results showing a link between hippocampal function, generalization performance, and total homocysteine levels. Importantly, our study is perhaps among the first to test the relationship between learning (and generalization) of rules and homocysteine levels in healthy controls and individuals with MCI.