Temporal Order Research Articles

The temporal relation of physical function with cognition and the influence of brain health in the oldest-old.

Introduction Physical function and cognition seem to be interrelated, especially in the oldest-old. However, the temporal order in which they are related and the role of brain health remain uncertain. Methods We included 338 participants (mean age 93.1 years) from two longitudinal cohorts: The UCI 90+ Study and EMIF-AD 90+ Study. We tested the association between physical function (Short Physical Performance Battery, gait speed and handgrip strength) at baseline with cognitive decline (MMSE, memory tests, Animal fluency, Trail Making Test (TMT) A and Digit Span Backward), and the association between cognition at baseline with physical decline (mean follow-up 3.3 years). We also tested whether measures for brain health (hippocampal, white matter lesion and gray matter volume) were related to physical function and cognition, and whether brain health was a common driver of the association between physical function and cognition by adding it as confounder (if applicable). Results Better performance on all physical tests at baseline was associated with less decline on MMSE, memory and TMT A. Conversely, fewer associations were significant but better scores on memory, TMT A and Digit Span Backward were associated with less physical decline. When adding measures for brain health as confounder, all associations stayed significant except for memory with gait speed decline. Discussion In the oldest-old, physical function and cognition are strongly related, independently of brain health. Also, the association between physical function and cognitive decline is more pronounced than the other way around, suggesting a potential for slowing cognitive decline by optimizing physical function.

Insight into the dynamics of protected and non-protected carbon pools in four soils with different land uses

Abstract Background and aims To provide insight into the patterns of soil organic matter decomposition, changes in the quantity of biopolymers and the correlation between them were followed using 2D correlation spectroscopy (2DCOS) FTIR. Methods Soil organic matter fractions with different vegetation/land use (grass, spruce, oak and arable) were examined in a 1-year laboratory incubation. The non-protected organic matter fraction was calculated in terms of particulate organic matter (POM), the carbon stabilized in aggregates as S + A (sand + aggregates), and the mineral-associated organic matter (MAOM) as the s + c (silt and clay) fraction. Results Forest soils (spruce, oak) exhibited high C and N accumulation in the POM fraction (48, 43% and 29, 22% for spruce and oak, respectively) due to the limited decomposition, caused by low pH and high soil C/N ratio. The 2DCOS analysis revealed that carbohydrate-protein and carbohydrate-lignin correlations could be observed most frequently during incubation. The carbohydrate-protein correlation was negative in all cases, for all fractions and for all vegetation types, which suggests biogeochemical linkage between these biopolymers. The temporal order of the spectral changes was widely varied for the vegetation types and especially for the SOM fractions. Lipid/Lignin → Carbohydrate or Lipid → Lignin/Carboxyl/Protein sequences were found for the protected carbon pools (S + A and s + c), possibly because of the readily available abundant N compounds present in MAOM. Conclusion Although lipids and lignin are considered as chemically stable materials that commonly remain constant during decomposition, these compounds were found to be very susceptible in all the fractions.

Soil Bacteria in Archaeology: What Could Rank Abundance Functions Tell Us About Ancient Human Impacts on Microbial Communities?

Metagenomic analysis of soil bacterial communities based on 16S rRNA reflects a typical community composition containing a low number of high-abundance types and a very high number of low-abundance types. Here, the formation of characteristic rank order functions of bacterial abundance is investigated by modelling the dynamics of soil bacterial communities, assuming a succession of different bacterial populations that grow rapidly and decay more slowly. We found that the characteristic shape of typical rank order functions is well reflected by simulations. In addition, our model allowed us to investigate strong disturbances in the soil, which could be expected in cases of strongly changing local environmental conditions in soil, e.g., after translocation and covering of soil material. Such events could lead to the formation of shoulders in the rank order functions. Abundance rank orders observed in cases of some archaeological soil samples do indeed show such a shoulder and could be well interpreted by simulated rank order functions. As a result, it can be concluded that the investigations herein support our hypothesis that abundance rank orders contain information about the temporal order of developing bacterial types in changing communities and thus store information about local environmental conditions in the past, including ancient humans’ impact on soil. This information can be used for interpretation of archeological findings and for reconstruction of different former human activities, as well as knowledge on the translocation of soil material in the past.

EXPRESS: On the interchangeability of presentation order for cause and effect: Experimental tests of cue and outcome density effects.

Studies of cue-outcome contingency learning demonstrate outcome-density effects: participants typically overestimate contingencies when the outcome event is relatively frequent. Equivalent cue-density effects occur, though these have been examined less often. Few studies have simultaneously examined both those event density effects or have manipulated the presentation order of the events,limiting knowledge of whether these phenomena share underlying principles-. We report three well-powered experiments to address those gaps. Participants judged the effectiveness of a medical treatment after viewing a series of pairings for two events, a cause (treatment given vs. not) and an effect (patient recovered vs. not). Experiment 1 manipulated both event densities independently. We then manipulated the presentation order for the cause and the effect, alongside a manipulation of effect density (Experiment 2a) or cause density (Experiment 2b). Experiment 1 found a large main effect of event-density (η_p^2 =.55), which was qualified by a significant interaction between event type and density level (η_p^2 =.10) whereby effect density had greater impact than cause density. Experiments 2a and 2b found effects for effect-density (η_p^2=.60) and cause-density (η_p^2=.31). The effects of cause-effect presentation order were always small and non-significant. We conclude that effect-density manipulations had substantial impact on contingency judgments, and cause-density manipulations less so. Moreover, it matters little which event (cause or effect) is seen first. These findings have implications for contingency, associative, probabilistic, and causal models of contingency judgment; primarily, that people may be more sensitive to the causal status of events than to their temporal order of presentation.

A simple but tough-to-beat baseline for fMRI time-series classification

Current neuroimaging studies frequently use complex machine learning models to classify human fMRI data, distinguishing healthy and disordered brains, often to validate new methods or enhance prediction accuracy. Yet, where prediction accuracy is a concern, our results suggest that precision in prediction does not always require such sophistication. When a classifier as simple as logistic regression is applied to feature-engineered fMRI data, it can match or even outperform more sophisticated recent models. Classification of the raw time series fMRI data generally benefits from complex parameter-rich models. However, this complexity often pushes them into the class of black-box models. Yet, we found that a relatively simple model can consistently outperform much more complex classifiers in both accuracy and speed. This model applies the same multi-layer perceptron repeatedly across time and averages the results. Thus, the complexity and black-box nature of the parameter rich models, often perceived as a necessary trade-off for higher performance, do not invariably yield superior results on fMRI.Given the success of straightforward approaches, we challenge the merit of research that concentrates solely on complex model development driven by classification. Instead, we advocate for increased focus on designing models that prioritize the explainability of fMRI data or pursue applicable objectives beyond mere classification accuracy, unless they significantly outperform logistic regression or our proposed model. To validate our claim, we explore possible reasons for the superior performance of our straightforward model by examining the innate characteristics of fMRI time series data. Our findings suggest that the sequential information hidden in the temporal order may be far less important for the accurate fMRI classification than the stand-alone pieces of information scattered across the frames of the time series.

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indicate that machine learning can be applied to momentary data to improve classification of SI. This study examined whether the classification accuracy of these models varies as a function of type of training data or characteristics of ideation. A total of 257 psychiatric inpatients completed a 3-week battery of ecological momentary assessment and measures of suicide risk factors. The accuracy of machine learning models in classifying the presence, duration, or intensity of ideation was compared across models trained on baseline and/or momentary suicide risk data. Relative feature importance metrics were examined to identify the risk factors that were most important for outcome classification. Models including both baseline and momentary features outperformed models with only one feature type, providing important information in both correctly classifying and differentiating individual characteristics of SI. Models classifying SI presence, duration, and intensity performed similarly. Results of this study may not generalize beyond a high-risk, psychiatric inpatient sample, and additional work is needed to examine temporal ordering of the relationships identified. Our results support using machine learning approaches for accurate identification of SI characteristics and underscore the importance of understanding the factors that differentiate and drive different characteristics of SI. Expansion of this work can support use of these models to guide intervention strategies.

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.

Temporal recording of mammalian development and precancer

Temporal ordering of cellular events offers fundamental insights into biological phenomena. Although this is traditionally achieved through continuous direct observations1,2, an alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible marks that enables retrospective temporal ordering3–5. Using a multipurpose, single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, with incorporation of cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during mouse embryonic development, unconventional developmental relationships between cell types and new epithelial progenitor states by their unique genetic histories. Analysis of mouse adenomas, coupled to multiomic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyclonal initiation in 15–30% of colonic precancers, showing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses, to explore the origins and timing of development and tumorigenesis in mammalian systems.

CMMamba: channel mixing Mamba for time series forecasting

Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Temporal ordering of cognitive impairment in Parkinson's disease patients based on disease progression models

IntroductionIdentifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution. MethodsWe enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline. ResultsAt baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients. ConclusionsNeuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ42) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF Aβ42 and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease.

Proteogenomic Landscape of Breast Ductal Carcinoma Reveals Tumor Progression Characteristics and Therapeutic Targets.

Multi-omics studies of breast ductal carcinoma (BRDC) have advanced the understanding of the disease's biology and accelerated targeted therapies. However, the temporal order of a series of biological events in the progression of BRDC is still poorly understood. A comprehensive proteogenomic analysis of 224 samples from 168 patients with malignant and benign breast diseases is carried out. Proteogenomic analysis reveals the characteristics of linear multi-step progression of BRDC, such as tumor protein P53 (TP53) mutation-associated estrogen receptor 1 (ESR1) overexpression is involved in the transition from ductal hyperplasia (DH) to ductal carcinoma in situ (DCIS). 6q21 amplification-associated nuclear receptor subfamily 3 group C member 1 (NR3C1) overexpression helps DCIS_Pure (pure DCIS, no histologic evidence of invasion) cells avoid immune destruction. The T-cell lymphoma invasion and metastasis 1, androgen receptor, and aldo-keto reductase family 1 member C1 (TIAM1-AR-AKR1C1) axis promotes cell invasion and migration in DCIS_adjIDC (DCIS regions of invasive cancers). In addition, AKR1C1 is identified as a potential therapeutic target and demonstrated the inhibitory effect of aspirin and dydrogesterone as its inhibitors on tumor cells. The integrative multi-omics analysis helps to understand the progression of BRDC and provides an opportunity to treat BRDC in different stages.

Aerobic exercise prevents and improves cognitive dysfunction caused by morphine withdrawal via regulating endogenous opioid peptides in the brain.

Morphine withdrawal leads to serious cognitive deficits in which dynorphins are directly involved. Recently, exercise has been shown to prevent and improve cognition dysfunction in a variety of ways. Meanwhile, exercise can regulate the endogenous opioid peptides including dynorphins. However, it remains unclear whether exercise influences cognitive dysfunction caused by morphine withdrawal via dynorphins. In the current study, we investigate the physiological mechanism of exercise prevention and improvement aganist cognition dysfunction caused by morphine withdrawal. Male, adult C57BL/6 mice were randomly divided into 5 groups : Saline control (WT), exercise (EXE), morphine withdrawl (MW), exercise + morphine withdrawl (EMW), morphine withdrawl + exercise (MWE). We established aerobic exercise prevention/improvement models, and conducted behavioral tests including Open field test (OFT), Temporal order memory test (TOM) and Y-maze. Through Western Blotting and immunofluorescence staining, we detected endogenous opioid peptides in hippocampus and mPFC. Compared with MW group, EMW group and MWE group showed the same performance as WT group in TOM and Y-maze, with correct object recognition and memory ability. In Western Blotting and immunofluorescence staining experiments, it indicated that EMW group reduced the expression of PDYN and its fluorescence intensity in hippocampus; MWE group reduced the expression of OPRK1 and its fluorescence intensity in mPFC. Our data suggest that aerobic exercise can both prevent and improve cognitive dysfunction caused by acute morphine withdrawal via respectively down-regulating PDYN in the hippocampus and down-regulating OPRK1 in the mPFC. They may become new targets for drugs development in the future.

How It Begins and Ends: Temporal Positions of Linguistic Indicators in Staged and Authentic Emergency Calls

AbstractThe utilisation of emergency calls to stage false and misleading narratives can assist offenders in concealing their criminal activity. The majority of emergency call analyses focus on detecting deception; however, investigating the complex temporal ordering of verbal indicators with a measure of proximity may offer new inductive investigative insights. The current research took a novel approach to analysing emergency calls by focusing on the temporal placement of verbal indicators with a measure of proximity coefficients. Results showed that several important differences existed in the temporal ordering of indicators that occur at the start and end of staged and authentic calls. These findings offer an insight as to influence of saliency and the competing priorities that exist between staged and authentic emergency calls. This is the first research to explore the temporal ordering of indicators in emergency calls and to provide a basis for its tentative application to interview strategies.

Neuronal sequences in population bursts encode information in human cortex.

Neural coding has traditionally been examined through changes in firing rates and latencies in response to different stimuli1-5. However, populations of neurons can also exhibit transient bursts of spiking activity, wherein neurons fire in a specific temporal order or sequence6-8. The human brain may utilize these neuronal sequences within population bursts to efficiently represent information9-12, thereby complementing the well-known neural code based on spike rate or latency. Here we examined this possibility by recording the spiking activity of populations of single units in the human anterior temporal lobe as eight participants performed a visual categorization task. We find that population spiking activity organizes into bursts during the task. The temporal order of spiking across the activated units within each burst varies across stimulus categories, creating unique stereotypical sequences for individual categories as well as for individual exemplars within a category. The information conveyed by the temporal order of spiking activity is separable from and complements the information conveyed by the units' spike rates or latencies following stimulus onset. Collectively, our data provide evidence that the human brain contains a complementary code based on the neuronal sequence within bursts of population spiking to represent information.

An exponential stochastic Runge–Kutta type method of order up to 1.5 for SPDEs of Nemytskii-type

Abstract For the approximation of solutions for stochastic partial differential equations, numerical methods that obtain a high order of convergence and at the same time involve reasonable computational cost are of particular interest. We therefore propose a new numerical method of exponential stochastic Runge–Kutta type that allows for convergence with a temporal order of up to $\frac{3}/{2}$ and that can be combined with several spatial discretizations. The developed family of derivative-free schemes is tailored to stochastic partial differential equations of Nemytskii-type, i.e., with pointwise multiplicative noise operators. We prove the strong convergence of these schemes in the root mean-square sense and present some numerical examples that reveal the theoretical results.

Myeloid neoplasm with concurrent BCR::ABL1 translocation, and JAK2 and SF3B1 co-mutations with severe fibrosis: a case report

Abstract Introduction/Objective Myeloproliferative neoplasms (MPNs) arise from hematopoietic stem cells with somatically altered tyrosine kinase signaling. Classification of MPNs is based on hematologic, histopathologic and molecular characteristics including the presence of BCR::ABL1 and JAK2 V617F. Methods/Case Report We present a 72-year-old male with a myeloid neoplasm and severe fibrosis, with concurrent BCR::ABL1 translocation, and co-mutations in JAK2 and SF3B1. Initially, the patient was diagnosed with anemia, thrombocytosis and folic acid deficiency and was prescribed supplemental folic acid. At follow up, normocytic anemia and thrombocytosis were seen. Bone marrow biopsy showed overtly fibrotic marrow with maturing trilineage hematopoiesis, markedly increased megakaryopoiesis and ring sideroblasts. Chromosome analysis and sequential GTG-fluorescence in situ hybridization (FISH) of metaphases using the dual-fusion three color probe BCR/ABL1- ASS1 revealed abnormal results. A t(9;22)(q34;q11.2) was detected along with an atypical FISH signal pattern with a single BCR::ABL1 fusion on the derivative chromosome 22 instead of the typical two fusions, resulting in loss of one BCR::ABL1 fusion signal including loss of the ASS1 on the derivative chromosome 9. Next-generation sequencing (NGS) analysis detected double driver mutations, JAK2 p. Val617Phe and SF3B1 p. Lys700Glu in the patient. JAK2 V617F mutation was further confirmed by real-time quantitative PCR (qPCR) with variant allele frequencies of 39.7%. Additionally, the patient was positive for BCR::ABL1 p190 fusion transcript (10%). The presence of JAK2 and SF3B1 co-mutations along with ring sideroblasts and the patient’s clinical presentation suggests the diagnosis of myelodysplastic/myeloproliferative neoplasm with thrombocytosis and SF3B1 mutation. Results (if a Case Study enter NA) NA Conclusion Although, the detection of t(9;22) by chromosome analysis is a diagnostic feature of chronic myeloid leukemia (CML), our case adds to the limited information on co-existence of BCR::ABL1 and JAK2 V617F with SF3B1 mutations in severe fibrosis. The frequency of co-occurrence, the temporal order of acquisition and clonal relationship of these alterations is poorly understood, and it is also unclear for this case since there is no prior bone marrow biopsy or diagnostic work up. Literature search of these rare cases show that some patients with similar findings progress to myelofibrosis as in this patient. It is possible that the presence of two mutated tyrosine kinase genes accelerates disease progression.

Energy change and Landauer’s principle in the interaction between qubit and quantum field theory

We give a general description of the system evolution under the interaction between qubit and quantum field theory up to the second order perturbation, which is also referred to as the simplified model of light-matter interaction. The results are classified into rotating and counter-rotating wave terms, the former corresponding to stimulated absorption and emission, and the latter to Unruh and anti-Unruh effects. We obtain not only the reduced density matrix of the qubit, but also the backreaction obtained by quantum field theory as the environment. The result shows that the energy variation of the quantum field theory is related to trajectory and the initial state of the qubit, the expectation values of the linear and quadratic field operators, and the temporal order product operator. When the qubit is in accelerated motion, the conventional Unruh effect causes the vacuum state to possess a “temperature”, which raises some doubts about the validity of Landauer’s principle. We prove that Landauer’s principle still holds for any state of motion.

Human olfactory perception embeds fine temporal resolution within a single sniff.

A sniff in humans typically lasts one to three seconds and is commonly considered to produce a long-exposure shot of the chemical environment that sets the temporal limit of olfactory perception. To break this limit, we devised a sniff-triggered apparatus that controls odorant deliveries within a sniff with a precision of 18 milliseconds. Using this apparatus, we show through rigorous psychophysical testing of 229 participants (649 sessions) that two odorants presented in one order and its reverse become perceptually discriminable when the stimulus onset asynchrony is merely 60 milliseconds (Cohen's d = 0.48; 95% confidence interval, (55, 59); 120-millisecond difference). Discrimination performance improves with the length of stimulus onset asynchrony and is independent of explicit knowledge of the temporal order of odorants or the relative amount of odorant molecules accumulated in a sniff. Our findings demonstrate that human olfactory perception is sensitive to chemical dynamics within a single sniff and provide behavioural evidence for a temporal code of odour identity.

The Impact of Adverse Childhood Experiences on Cognitive Control Across the Lifespan: A Systematic Review and Meta-analysis of Prospective Studies.

Adverse childhood experiences (ACEs) are strongly associated with impaired cognitive control, yet research on ACEs' effects across cognitive control domains-working memory, cognitive flexibility, and inhibitory control-remains sparse. This systematic review and meta-analysis evaluated the overall impact of ACEs on each of these cognitive control domains and explored moderating factors such as age, gender, cognitive control paradigms, and ACEs subtypes based on the dimensional model of adversity and psychopathology. A database search was conducted in SCOPUS, MedLine, PsycINFO, and Web of Science. Only prospective studies were included to ensure temporal order inferences, with at least two data collection points, assessing ACEs at baseline (T1) and cognitive control during follow-up (T2). Thirty-two studies (N = 26,863) producing 124 effect sizes were analyzed. Three-level meta-analyses revealed small-to-medium negative associations between ACEs and overall cognitive control (g = -0.32), and in each domain: working memory (g = -0.28), cognitive flexibility (g = -0.28), and inhibitory control (g = -0.32). The negative associations between ACEs and cognitive control were consistent across age, gender, and cognitive control paradigms. ACEs subtypes moderated the association with cognitive flexibility (p = .04) but not working memory or inhibitory control. Specifically, the deprivation subtype exhibited a stronger negative association with cognitive flexibility compared to threat and threat-and-deprivation subtypes. These findings highlight the pervasive negative impact of ACEs on cognitive control across ages and emphasize the need for targeted interventions. Implications, current gaps, limitations in research, and future study recommendations are discussed.

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.