Temporal Order Of Events Research Articles

Multisensory temporal processing in schizophrenia and bipolar disorder: implications for psychosis

Structuring sensory events in time is essential for interacting with the environment and producing adaptive behaviors. Over the past years, the microstructure of temporality received increasing attention, recognized as a fundamental factor influencing cognitive, affective, and social abilities, whose alteration can underlie the etiopathogeneses of some clinical symptoms in psychiatric disorders. The present research investigated multisensory temporal processing in individuals with schizophrenia (N = 21), bipolar disorder (N = 20) and healthy controls (N = 21) in order to explore a plausible link between multisensory alterations in the temporal order of events and the psychopathological dimensions underlying psychosis. We asked participants to temporally order audio-tactile, visual-tactile, and audio-visual stimuli, and we administered different psychopathological scales to explore depressive, manic and psychotic symptoms. Results demonstrated that both subjects with schizophrenia and bipolar disorder are less precise in temporal order judgment independently of the sensory modalities involved. Interestingly, reduced precision in temporal processing of patients is positively associated with the presence and severity of positive symptoms. Our findings support the hypothesis that low-level sensory alterations in temporal structure may contribute to the emergence of clinical symptoms such as delusions, hallucinations, and disorganized behaviors.

A preserved neural code for temporal order between memory formation and recall in the human medial temporal lobe.

Temporal memory enables us to remember the temporal order of events happening in our life. The human medial temporal lobe (MTL) appears to contain neural representations supporting temporal memory formation, but the cellular mechanisms that preserve temporal order information for recall are largely unknown. Here, we examined whether human MTL neuronal activity represents the temporal position of events during memory formation and recall, using invasive single and multi-unit recordings in human epilepsy patients (n = 19). Participants freely navigated a virtual environment in order to explore and remember locations and temporal positions of objects. During each exploration period, they sequentially encountered two or three different objects, placed in different locations. This allowed us to examine single- and multi-unit neuronal firing rates (FR) as a function of the temporal position the objects were presented in. We found that a significant number of multi-units and single-units in various MTL regions including the hippocampus showed selectivity to the temporal position of objects during the exploration period. During recall, patients were asked to indicate which one of two objects from the same trial was found latter. Neural firing rates during recall showed a selectivity supporting recall of temporal positions. Interestingly, most of the selective single-units that stayed selective during encoding and recall preserved their temporal position preference. Our results thus suggest that neuronal activity in the human MTL contains a preserved neural code for temporal order in memory formation and recall.

Realismo, Relatividad General y el Gato de Schrödinger

The present work examines the nature of reality, including the possibility of freedom, in the framework of modern physics. Additionally, it proposes a reform for the metaphysics of realism.For realism, the world is the way it is independent of the mind. However, general relativity supposesthat the speed of objects and the temporal order of events depend on the frame of referenceadopted. Which frame of reference is adopted depends on human interests. However, there are still physical facts independent of the frame of reference: the speed of light, spacetime distance,and the equivalence of matter and energy, amongst others. On the other hand, the CopenhagenInterpretation supposes that quantum states are in a superposition that is only realized at themoment of observation, leading to the implication that Schrödinger’s cat is both alive and deaduntil observed. However, less incredible realist possibilities are analysed. Modern physics, whetherdeterminist or indeterminist, also threatens the possibility of freedom. The compatibility of freedom as self-government and modern physics is analysed and developed. Although central aspectsof realist metaphysics are conserved, a philosophical-scientific conception of the universe that integrates mental beings within it emerges, which supposes a reform for standard scientific realism. The existence of facts independent of what we happen to think is saved alongside ineliminable mental phenomena.

SMoTeF: Smurf money laundering detection using temporal order and flow analysis

Smurfing in financial networks is a popular fraud technique in which fraudsters inject their illegal money into the legitimate financial system. This activity is performed within a short period of time, with recurring transactions and multiple intermediaries. A major problem of existing graph-based methods for detecting smurfing is that they fall short of retrieving accurate fraud patterns. Consequently, the result is numerous non-fraudulent patterns alongside a few fraud patterns, causing a high false-positive rate. To alleviate this problem, we propose SMoTeF, a framework that extends existing graph-based smurf detection methods by distinguishing fraudulent smurfing patterns from non-fraudulent ones, thus significantly reducing the false-positive ratio. The core of the approach is a novel algorithm based on computing maximum temporal flow within temporal order of events. In order to evaluate the approach, a framework for injecting various smurfing patterns is developed, and experimental results on three real-world datasets from different domains show that SMoTeF significantly improves on the effectiveness of the state-of-the-art baseline, with only marginal runtime overhead.

Learning topic emotion and logical semantic for video paragraph captioning

Video paragraph captioning aims to generate multiple descriptive sentences for videos, which strive to replicate human writing in accuracy, logicality, and richness. However, current research focuses on the accuracy and temporal order of events, ignoring emotion and other critical logical relations embedded in human language, such as causal and adversative relations. The ignorance impairs the reasonable transition across generated event descriptions and restricts the vividness of expression, resulting in a gap from the standard of human language. To resolve the problem, a framework that integrates logic and emotion representation learning is proposed to narrow the gap. Concretely, a large-scale inter-event relation corpus is constructed based on the EMVPC dataset. This corpus is named EMVPC-EvtRel (standing for “EMVPC-Event Relations”) and contains six widely-used logical relations in human writing, 127 explicit inter-sentence connectives, and over 20,000 pairs of event segments with newly annotated logical relations. A logical semantic representation learning method is developed for recognizing the dependencies between visual events, thereby enhancing the characteristics of video contents and boosting the logicality of generated paragraphs. Moreover, a fine-grained emotion recognition module is designed to uncover emotion features embedded in videos. Finally, experimental results on the EMVPC dataset demonstrate the superiority of the proposed method compared to existing state-of-the-art approaches.

The Temporal Evolution of Chromosome 1q Gain and Hyperdiploidy and Its Impact on Clinical Outcomes in Multiple Myeloma

INTRODUCTION: Multiple myeloma (MM) evolution is shaped by the acquisition and selection of distinct genomic events over time. While various temporal patterns have been identified, the precise chronological order in which key clonal genomic defining events are acquired, and their specific impact on clinical outcomes remain to be fully elucidated. METHODS: To decipher the genomic life history of MM and its impact on clinical outcomes, we interrogated the high coverage (80X) whole genome sequencing (WGS) of 378 MM patients. Overall, 319 and 59 WGS were generated from samples collected at diagnosis and at relapse, respectively. Newly diagnosed MM (NDMM) patients were treated upfront with proteosome inhibitors, lenalidomide, dexamethasone, +/- monoclonal antibodies. To estimate the relative and absolute temporal relationship among clonal events, we leveraged the molecular time approach, based on the corrected ratio between duplicated and non-duplicated single nucleotide variants (SNV). RESULTS: Overall, 300/378 (79%) patients had at least one large chromosomal gain defined as >1Mb with >50 clonal SNV eligible for molecular time analysis, for a total of 1493 events tested. Consistent with recent findings, using clock-like mutational signatures SBS1-SBS5, we estimated that the first gain in MM is typically acquired around the second or third decade of life, with a median time lag of approximately 33 years between acquisition and diagnosis. In 158/178 (89%) of hyperdiploid (HRD) patients, all trisomies of odd-numbered chromosomes were acquired simultaneously. Among patients with canonical IGH translocations and HRD, the molecular time when trisomies were acquired was usually late compared to HRD without canonical IGH translocations (p<0.0001). Next, we examined the temporal relationship between IGH canonical translocations and gains linked to the same IGH canonical translocation in 29 patients. We consistently observed that when IGH canonical translocations co-occurred with HRD (n=7), and/or 1q gain (n=8) in the same patient, the canonical IGH translocation was always found to be the initiating event, occurring earlier than the chromosomal gains. Interestingly, in 104/178 (58.4%) HRD patients, the earliest multi-gain event was not always restricted to odd chromosomes, but in involved other non-HRD chromosomes such as: 6p (n=43) and 1q (n=32), demonstrating that gain 1q, which is assumed to occur at progression, can also be acquired very early in disease life history. A total of 92 and 21 patients with 1q gain and 1q amp (>3 copies) were identified, respectively. In 14/21 (66%) with 1q amp, the first and second 1q extra copies were acquired in two distinct and independent time windows. Interestingly, patients with early 1q gain showed the same clinical outcomes as 1q amp, and inferior progression free survival outcomes compared to late 1q gain (p=0.01; Figure 1). In 18/21 1q amp the first 1q duplication was acquired early, suggesting that the adverse prognostic effect of 1q gain/amp in NDMM may be more dependent on the timing of its acquisition rather than the number of extra copies gained. To investigate late temporal patterns and differentiate selection versus acquisition of large gains at relapse after autologous stem cell transplant (ASCT), we interrogated 25/59 (42%) RRMM patients exposed to melphalan with detectable SBS-MM1. To establish the temporal order of events, we employed the melphalan mutational signature, SBS-MM1, as a unique temporal barcode (Diamond et al. Blood 2023). Overall, we observed that 21% of all loss of heterozygosity (LOH) events and 15% of all gains were acquired after ASCT (i.e., SBS-MM1 mutations were only detectable in the pre-gain/duplicated SNV). Notably, among these events, 1q gain was acquired after ASCT in 3/4 patients, indicating that the acquisition of 1q gain can occur even after administration of first-line therapy. CONCLUSIONS: In our study, we have uncovered distinct temporal patterns in the acquisition of early genomic drivers in MM. These patterns contribute to the reconstruction and better understanding of the life history of MM and may hold potential clinical and prognostic implications.

Adjustment of Anticipatory Covariates in Retrospective Surveys: An Expected Likelihood Approach

We address an inference issue where the value of a covariate is measured at the date of the survey but is used to explain behavior that has occurred long before the survey. This causes bias because the value of the covariate does not follow the temporal order of events. We propose an expected likelihood approach to adjust for such bias and illustrate it with data on the effects of educational level achieved by the time of marriage on risks of divorce. For individuals with anticipatory educational level (whose reported educational level was completed after marriage), conditional probabilities of having attained the reported level before marriage are computed. These are then used as weights in the expected likelihood to obtain adjusted estimates of relative risks. For our illustrative data set, the adjusted estimates of relative risks of divorce did not differ significantly from those obtained from anticipatory analysis that ignores the temporal order of events. Our results are slightly different from those in two other studies that analyzed the same data set in a Bayesian framework, though the studies are not fully comparable to each other.

Multiple memory systems for efficient temporal order memory.

We report distinct contributions of multiple memory systems to the retrieval of the temporal order of events. The neural dynamics related to the retrieval of movie scenes revealed that recalling the temporal order of close events elevates hippocampal theta power, like that observed for recalling close spatial relationships. In contrast, recalling far events increases beta power in the orbitofrontal cortex, reflecting recall based on the overall movie structure.

ToMExO: A probabilistic tree-structured model for cancer progression.

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method's performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.

Identification of transient intermediates during spliceosome activation by single molecule fluorescence microscopy

Spliceosome activation is the process of creating the catalytic site for RNA splicing and occurs de novo on each intron following spliceosome assembly. Dozens of factors bind to or are released from the activating spliceosome including the Lsm2-8 heteroheptameric ring that binds the U6 small nuclear RNA 3'-end. Lsm2-8 must be released to permit active site stabilization by the Prp19-containing complex (NineTeen Complex, NTC); however, little is known about the temporal order of events and dynamic interactions that lead up to and follow Lsm2-8 release. We have used colocalization single molecule spectroscopy (CoSMoS) to visualize Lsm2-8 dynamics during activation of Saccharomyces cerevisiae spliceosomes in vitro. Lsm2-8 is recruited as a component of the tri-snRNP and is released after integration of the Prp19-containing complex (NTC). Despite Lsm2-8 and the NTC being mutually exclusive in existing cryo-EM structures of yeast B complex spliceosomes, we identify a transient intermediate containing both ([Formula: see text]) and provide a kinetic framework for its formation and transformation during activation. Prior to [Formula: see text] assembly, the NTC rapidly and reversibly samples the spliceosome suggesting a mechanism for preventing NTC sequestration by defective spliceosomes that fail to properly activate. In complementary ensemble assays, we show that a base-pairing-dependent ternary complex can form between Lsm2-8 and U2 and U6 helix II RNAs. We propose that this interaction may play a role in formation of transient spliceosome intermediates formed during activation.

Hierarchical planning with state abstractions for temporal task specifications

We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command “go to the kitchen before going to the second floor” contains spatial abstraction, given that “floor” consists of individual rooms that can also be referred to in isolation (“kitchen”, for example). There is also a temporal ordering of events, defined by the word “before”. Previous works have used syntactically co-safe Linear Temporal Logic (sc-LTL) to interpret temporal language (such as “before”), and Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as “kitchen” and “second floor”), separately. To handle both types of commands at once, we introduce the Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95 % of path planning tasks, and this number only increases as the size of the environment domain increases. In a cleanup world domain, AP-MDP performs faster in over 98% of tasks. We also present a neural sequence-to-sequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on two drones, demonstrating that our approach enables robots to efficiently solve temporal commands at different levels of abstraction in both indoor and outdoor environments.

Event segmentation reveals working memory forgetting rate

SummaryWe encounter the world as a continuous flow and effortlessly segment sequences of events into episodes. This process of event segmentation engages working memory (WM) for tracking the flow of events and impacts subsequent memory accuracy. WM is limited in how much information (i.e., WM capacity) and for how long the information is retained (i.e., forgetting rate). In this study, across multiple tasks, we estimated participants’ WM capacity and forgetting rate in a dynamic context and evaluated their relationship to event segmentation. A U-shaped relationship across tasks shows that individuals who segmented the movie more finely or coarsely than the average have a faster WM forgetting rate. A separate task assessing long-term memory retrieval revealed that the coarse-segmenters have better recognition of temporal order of events compared to the fine-segmenters. These findings show that event segmentation employs dissociable memory strategies and correlates with how long information is retained in WM

Timing Aspects in Causality Analysis with Multilevel Flow Modelling

Temporal aspects of multilevel flow modelling (MFM) are important for reasoning about causes and consequences. In particular real time reasoning about sensor data are dependent on proper temporal ordering of events in order to cope with plant dynamics. The purpose of the present paper is to contribute to the further development of the temporal aspects of MFM by explaining how time stamps associated to the measured signals can be used to enhance the causality analysis and infer the possible causes of a sequence of alarms.

Causality in Reversed Time Series: Reversed or Conserved?

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.

Evidence for a general impairment of auditory and visual temporal order judgment in children with reading disabilities

Objectives: This study examined visual and auditory processing of children with reading disabilities (RD) to determine whether they show an impaired ability to judge a reading-related process such as temporal order of events and clarify whether or not this deficit is linked to rapid processing deficit. Method: The performance of 16 children with RD was compared with the performance of children in two control groups – one for chronological age (CA) and one for the same reading level –, doing visual and auditory temporal order tasks, both with linguistic and non-linguistic stimuli with inter-stimuli-intervals of 50, 150, or 300 ms. Results: The RD group performance was lower than the performance of the CA group in tasks requiring auditory temporal order processing for linguistic and non-linguistic stimuli. Regarding visual tasks, the RD group performed worse than both control groups in processing non-linguistic stimuli. In general, performance in every group decreased with decreasing inter-stimulus-interval (ISIs), suggesting that children with RD do not have impairments in the speed of perceptual processing. Conclusions: The perceptual problems of children with RD are better explained by temporal order processing problems than by difficulties in rapid processing. Inclusion of temporal order processing tasks in the evaluation of children with RD is recommended.

Memory for Dynamic Events When Event Boundaries Are Accentuated With Emotional Stimuli

Event boundaries are important moments throughout an ongoing activity that influence perception and memory. They allow people to parse continuous activities into meaningful events, encode the temporal sequence of events and bind event information together in episodic memory (DuBrow & Davachi, 2013). Thus, drawing attention to event boundaries may facilitate these important perceptual and encoding processes. In the current study, we used emotionally arousing stimuli to guide attention to event boundaries because this type of stimulus has been shown to influence perception and attention. We evaluated whether accentuating event boundaries with commercials improves memory and whether emotional stimuli further enhance this effect. A total of 97 participants watched a television episode in which we manipulated commercial break locations (boundary, non-boundary, no commercial) and the type of commercial (emotional, neutral) and then completed memory tasks. Overall, placing emotionally arousing commercials at event boundaries increased memory for the temporal order of events, but no other effects of accentuating event boundaries were observed. Thus, drawing attention to event boundaries—via emotionally charged commercials—increases the likelihood that people will perceive the change in events, update their mental model accordingly and better integrate temporal information from the just-encoded event.

The Interplay of Having an Abortion, Relationship Satisfaction, and Union Dissolution

This study researches the associations between having an abortion, relationship satisfaction, and union dissolution. Empirical evidence on this topic is scarce, and there is a pronounced lack of studies analysing longitudinal data: Most previous studies have used data from women recruited from abortion clinics who are about to undergo an abortion, and therefore do not incorporate a prospective measure of relationship satisfaction pre-pregnancy. Panel studies, on the other hand, collect prospective data on various topics and allow for the estimation of more advanced models that can help identify causal mechanisms. Using data from the German Family Panel pairfam in combination with pooled logistic regressions, discrete-time event history models, as well as fixed effects regression models, this study compares relationships up to nine years before having had an abortion and eight years afterwards. The findings of the analyses can neither confirm that relationship satisfaction acts as a confounding factor that influences both the likelihood of terminating a pregnancy and union dissolution, nor as a mediating factor between having an abortion and union dissolution. A negative effect of having an abortion on relationship satisfaction appears to be only temporary. In the year of an abortion, relationship satisfaction decreases slightly. In the following years, a significant difference in relationship satisfaction to pre-abortion years is no longer visible. By using panel data, the temporal order of events can be retraced, resulting in the discovery that relationship satisfaction and union dissolution do not change drastically from pre-abortion values after having an abortion.
 * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

A dynamic model for the mutual constitution of individuals and events

Abstract We argue and show that a recently derived class of relational hyperevent models (RHEM) may be adopted to extend the sociological concept of duality by linking it to empirical data containing information on the temporal order of events. We show how RHEMs may be specified to predict the likelihood that combinations of individuals of any size will jointly participate in future events, conditional on their history of participation in past events. We show, further, how RHEMs may support hypothesis testing about competing mechanisms driving participation in events. Finally, we show how RHEMs may be used to establish the location of the events that actually happened in the much larger space of all the possible events that could have happened, but did not. We illustrate the empirical value of RHEMs using a canonical dataset containing information on the participation of 18 women in 14 time-ordered events. We provide dynamic network visualizations to link empirical estimates of the model parameters to qualitative insight on the dynamics of the mutual constitution of individuals and events. While RHEMs are also applicable to large networks (e.g. coauthorship networks), using a small canonical dataset allows us to examine in greater detail the model’s implications for each and every observed event and to identify the location of each event participant in the network of previous events. Scaling down our model to examine a small dataset affords a more detailed understanding of the link between quantitative model results expressed as parameter estimates, and the qualitative features of the original observations.

Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk

ABSTRACT We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not follow the temporal order of events. We propose a dynamic Bayesian approach for modelling jointly the anticipatory covariate and the event of interest, and allowing the effects of the anticipatory covariate to vary over time. The issues are illustrated with data on the effects of education attained by the survey-time on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to elevated relative risks of divorce across educational levels. The results are partially in accordance with previous findings based on analyses of the same data set. More importantly, our findings provide new insights in that the bias due to anticipatory covariates varies over marriage duration.

Methamphetamine Activates Trace Amine Associated Receptor 1 to Regulate Astrocyte Excitatory Amino Acid Transporter-2 via Differential CREB Phosphorylation During HIV-Associated Neurocognitive Disorders

Methamphetamine (METH) use, referred to as methamphetamine use disorder (MUD), results in neurocognitive decline, a characteristic shared with HIV-associated neurocognitive disorders (HAND). MUD exacerbates HAND partly through glutamate dysregulation. Astrocyte excitatory amino acid transporter (EAAT)-2 is responsible for >90% of glutamate uptake from the synaptic environment and is significantly decreased with METH and HIV-1. Our previous work demonstrated astrocyte trace amine associated receptor (TAAR) 1 to be involved in EAAT-2 regulation. Astrocyte EAAT-2 is regulated at the transcriptional level by cAMP responsive element binding (CREB) protein and NF-κB, transcription factors activated by cAMP, calcium and IL-1β. Second messengers, cAMP and calcium, are triggered by TAAR1 activation, which is upregulated by IL-1β METH-mediated increases in these second messengers and signal transduction pathways have not been shown to directly decrease astrocyte EAAT-2. We propose CREB activation serves as a master regulator of EAAT-2 transcription, downstream of METH-induced TAAR1 activation. To investigate the temporal order of events culminating in CREB activation, genetically encoded calcium indicators, GCaMP6s, were used to visualize METH-induced calcium signaling in primary human astrocytes. RNA interference and pharmacological inhibitors targeting or blocking cAMP-dependent protein kinase A and calcium/calmodulin kinase II confirmed METH-induced regulation of EAAT-2 and resultant glutamate clearance. Furthermore, we investigated METH-mediated CREB phosphorylation at both serine 133 and 142, the co-activator and co-repressor forms, respectively. Overall, this work revealed METH-induced differential CREB phosphorylation is a critical regulator for EAAT-2 function and may thus serve as a mechanistic target for the attenuation of METH-induced excitotoxicity in the context of HAND.