The results of these analyses show that the presence of GL261 tumor-conditioned medium resulted in increased production of Th1, inflammatory and inhibitory cytokines by spleen cells from control mice and from vaccinated glioma-bearing mice. In contrast, spleen cells of tumor-bearing, mock-vaccinated mice produced lower levels of cytokines in the presence of tumor-conditioned media. However, these results also show that there was not a heightened level of cytokine production in the presence of tumor-conditioned medium by spleen cells of vaccinated mice over the production by spleen cells of control mice. Overall, these results show that vaccination slows growth of the GL261 tumors to the point where GL261-vaccinated mice do not show the signs of morbidly or splenic dysfunction exhibited by unvaccinated, late stage glioma-bearing mice.Prior studies had shown the clinical efficacy of a semi-allogeneic glioma vaccine in mice with lethal GL261 gliomas. This was confirmed in the present study. As subcutaneous vaccination resulted in protection against tumor in the brain, the present study assessed the impact of this vaccination of mice bearing established GL261 brain gliomas on their cytokine production upon in vitro exposure to tumor-derived products. Mice with established GL261 brain gliomas were vaccinated subcutaneously with H-2 Glioblastoma multiforme (GBM) is the deadliest of brain tumors and is one of a group of tumors referred to as gliomas. GBMs make up approximately 15% of all primary brain tumors [Standard treatment for GBM is surgery, followed by combined radiation therapy and chemotherapy. GBM’s capacity to invade and infiltrate normal surrounding brain tissue makes complete resection impossible. After surgery, combined chemo-radiation is used to kill residual tumor cells and to delay recurrence. Temozolomide was approved by the Food and Drug Administration (FDA) in 2005 for treatment of adult GBM; subsequently, the FDA approved Avastin (Bevacizumab) for treating GBM. Nevertheless, with standard of care therapy, the median survival of children and adults with GBM is 15 months, and the five-year survival rate is approximately 10% . While tThe powerful immune reactivity leading to transplant rejection can be attributed to the capacity of T-cells to recognize allogeneic major histocompatibility complex (MHC) molecules as intact structures on the surface of foreign cells . This isThe results of studies indicating the effectiveness of semi-allogeneic cancer vaccines in several cancer models suggest that these vaccines could also be tested as an adjuvant treatment for GBM. Using an orthotopic C57BL/6 mouse model of GBM, our prior study showed that subcutaneous injection of an irradiated GL261×RAG semi-allogeneic hybrid vaccine into mice with established GL261 brain cancer induced an effective anti-tumor clinical response . To furtOur experimental observations suggest that spleen cells from vaccinated mice exhibited an intact capacity to produce a broad spectrum of cytokines in the presence of tumor-derived products (indistinguishable from that observed in spleen cells of control mice). Spleen cells from mock-vaccinated tumor-bearing mice produced significantly lower levels of cytokines in the presence of mediators derived from glioma cells than did spleen cells of vaccinated mice.Orthotopically implanted GL261 tumor cells in mice is lethal. In the present study, animals were euthanized once they became ill due to tumor burden. For mice that received the mock vaccine, the interval between tumor implantation and the required euthanasia was between 35 and 70 days. This time line was similar to what was previously described in studies to assess survival times of mock-vaccinated versus tumor-vaccinated mice bearing GL261 brain tumors . To compg: control vs. sham vaccinated = 0.566; control vs. vaccinated = 0.753; sham vaccinated vs. vaccinated = 1.467). The presence of tumor-conditioned medium increased production of IL-2 by spleen cells from the control mice and from the vaccinated mice almost two-fold, while IL-2 production by the spleen cells from the mock-vaccinated tumor-bearing mice was not significantly affected by the conditioned medium. The results were similar for production of the other Th1 cytokine that was measured, IFN-γ, except that the basal level produced by unstimulated spleen cells from the mock-vaccinated tumor-bearing mice was less than the level produced by spleen cells of control mice. The effect sizes for comparisons of IFN-γ levels were medium to large . Because of the lower basal level of IFN-γ produced by spleen cells of mock-vaccinated tumor-bearing mice, the stimulation index (fold increase) in response to tumor-conditioned medium was greater than for spleen cells from the other groups of mice. Nevertheless, these levels were lower than those measured for spleen cells from either normal control or vaccinated mice.Production of cytokines was compared between control mice and tumor-bearing mice that were either mock-vaccinated with phosphate-buffered saline or had received irradiated semi-allogeneic vaccine following the engraftment of GL261 cancer cells in the brain. These cytokine measurements were performed for each mouse individually. Spleen cells from each mouse were cultured in either control medium (unstimulated) or medium containing supernatants from cultured GL261 tumor cells (stimulated). Levels of the Th1 cytokine IL-2 were similar for unstimulated spleen cells from each of the three groups of mice . CompariThese results show that glioblastoma-bearing mice have reduced levels of Th1 cytokine production in the presence of tumor-conditioned medium, but the cytokine production in those that were vaccinated is maintained at levels similar to that of control mice. However, these results also show that the increased production of cytokines in the presence of tumor-conditioned medium is not specific to vaccine exposure since the spleen cells from vaccinated mice produced similarly increased levels of Th1 cytokines as did spleen cells of control mice that had not been exposed to either tumor or to the vaccine.g: control vs. sham vaccinated = 5.262; control vs. vaccinated = 1.956; sham vaccinated vs. vaccinated = 2.435). The effect sizes for comparisons of TNF-α levels were large . In the presence of tumor-conditioned medium, spleen cells from each of the groups of mice were stimulated to produce increased levels of both of the inflammatory mediators, with the greatest inflammatory cytokine production being by spleen cells of control mice and by spleen cells of vaccinated mice. The least level of production of either IL-6 or TNF-α was by spleen cells from mock-vaccinated tumor-bearing mice. As was seen for the Th1 cytokines . While spleen cells of both control mice and vaccinated mice produced increased levels of IL-4 in the presence of the tumor-conditioned medium, the amount produced was less than that seen for IFN-γ . Tumor-conditioned medium stimulated increased IL-10 production by spleen cells of control mice and vaccinated mice, but not by spleen cells from tumor-bearing, mock-vaccinated mice  inoculation of a semi-allogeneic GL261 glioma vaccine in mice with established GL261 tumors . AnalysiThe presence of activated microglia at the tumor-inoculation site in vaccinated mice, but the absence of a prominent T-cell infiltrate, prompted studies to assess the peripheral immune impact of tumor presence and vaccination of glioma-bearing mice on their cytokine production in response to tumor-derived products. The rationale for these studies was based on demonstrations of tumor-induced immune dysfunction in a variety of tumors in both humans and in animal models ,10,11,12The results of the present study showed that the presence of tumor-derived products results in increased cytokine production in either control mice or vaccinated mice. However, spleen cells of tumor-bearing, mock-vaccinated mice produced lower levels of cytokines in the presence of tumor-conditioned medium, consistent with tumor-induced immune suppression. This was most consistently seen for Th1 and inflammatory cytokines, with results of inhibitory cytokines being variable. The reduced cytokine production by spleen cells of tumor-bearing mock-vaccinated mice could not be explained by differences in spleen cell numbers as equal numbers of cells were plated for each of the groups of mice. The present study also showed that both the basal cytokine profile and the stimulated cytokine production by spleen cells of vaccinated mice in the presence of tumor-derived products were similar to those of control mice. While the vaccination restored a normal cytokine phenotype, there was no detectable increase in cytokine production in the presence of tumor-derived products over that seen for normal spleen cells that had not been previously exposed to tumor products. In fact, it seemed ironic that, in the presence of tumor-conditioned medium, spleen cells from control and vaccinated mice not only produced Th1 and inflammatory cytokines, but also produced increased levels of immune inhibitory cytokines. The differences in cytokine production by mock-vaccinated and tumor-vaccinated mice do not represent an across-the-board inability of mock-vaccinated mice to produce cytokines since basal levels of IL-2 that are produced are similar between all of the groups of mice and basal levels of IL-4 that are produced by cells of mock-vaccinated mice are higher than for cells from control mice. In response to stimulation, similar levels of IL-4 are produced by cells from each of the groups of mice. For the purpose of consistency with the clinical analyses that were previously reported with this vaccine model , an aliqAs a multitude of cytokines can cross from the periphery into the brain, studies are needed to test if vaccination can stimulate immune cell release of cytokines that, in turn, lead to neuroinflammation at the tumor site. The most immediate approach to link the effectiveness of peripheral vaccination with reactivity to brain cancer would be to conduct studies measuring circulating cytokine levels in mock-vaccinated and tumor-vaccinated mice. Following determination of circulating cytokine levels, causality between these cytokine phenotypes and any clinical responses would need to be demonstrated through studies such as to neutralize key candidate cytokines with antibody or to use select cytokine knock-out mice.The in vitro studies described in this report, while correlative, are first steps to better understand the vaccine-driven immunological processes which confer protection against GBM. Continuation of these initial studies is warranted by the recognition that immunotherapy has emerged as an important adjuvant in the therapeutic armamentarium of clinicians against GBM , and supd-glucose and l-glutamine, supplemented with 10% fetal bovine serum  and 1× antibiotic antimycotic solution (Sigma) containing penicillin, streptomycin and amphotericin B.Cell culture media consisted of 1× Dulbecco’s modified Eagle’s medium , containing 4.5 g/L b haplotype) was used for inoculation into mice in this study as in the prior study. While these tumor cells had been engineered to express luciferase to enable clinical assessment of tumor development in the brain, the impact of luciferase was minimized in the experimental design by inoculating the same cell stock into mice that were to be either mock-vaccinated or receive the semi-allogenic vaccine. GL261 cells were maintained by passage in DMEM supplemented with 10% of fetal bovine serum. Generation of the RAGxGL261 semi-allogeneic hybrid vaccine was previously described [d haplotype), were mixed in serum-free medium containing 50 μM sodium dodecyl sulfate and centrifuged at 300× g for 5 min. The mixed cell pellet was then suspended in 50% polyethylene glycol (PEG)-1450 over a 1 min period while gently stirring. The cell suspension was then diluted over a 2 min period with DMEM supplemented with 10% FBS. After cell washing, cells were plated in selection medium containing 400 µg/mL G418 and HAT supplement (Invitrogen). Under these culture conditions only RAG×GL261 semi-allogeneic somatic cell hybrids can survive, since RAG-neo cells are killed by aminopterin and GL261 cells are killed by G418.For the purpose of consistency with the clinical analyses that we were previously reported with this tumor model , an aliqescribed . Briefly4/mouse) were injection into the right cerebral hemisphere using a 5 μL Hamilton micro-syringe under mechanical control to avoid brain injuries during the procedure. Three days after tumor inoculation, treated mice were injected s.c. with lethally irradiated (30 Gray) GL261×RAG-neo hybrids (106 cells per mouse) in 0.5 mL PBS, and mock-vaccinated mice were injected s.c. with 0.5 mL phosphate-buffered saline alone. Mice were checked daily until the end of the experiment. As mock-vaccinated mice exhibited signs of tumor burden, they were euthanized along with control mice and vaccinated tumor-bearing mice. Thus, each time the study was conducted, the time between tumor inoculation and when mice were sacrificed was the same for both the mock-vaccinated and the tumor-vaccinated mice. At time of sacrifice, spleens from mice in each group were collected and used as described below. Spleen cells production of cytokines was measured for each mouse individually. In total, spleen cell cultures were established for cytokine assessment from 6 control mice, 4 mock-vaccinated glioma-bearing mice, and 13 glioma-bearing mice that received the semi-allogeneic vaccine. This was conducted over the course of 4 separate experiments.Mouse studies were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) and were conducted in accordance with all applicable national and institutional guidelines for the care and use of animals. Ten to twelve week-old C57BL/6 male mice  were anesthetized by i.p.  injection of 90 mg/kg ketamine  and 10 mg/kg xylazine . Mice were then positioned in a Kopf stereotaxic frame for intracranial injection into the striatum . GL261 cells suspended in PBS (2 × 106 cells per mouse) in PBS [g. The cell pellet was suspended in 2 mL of red blood cells lysing buffer (ammonium-chloride-potassium (ACK), LONZA, Allendale, NJ, USA) for 3 min and diluted with 50 mL Hank’s balanced salt solution (HBSS) to inactivate ACK. The cell suspension freed of red blood cells was used for in vitro studies.Spleens were collected at sacrifice from three groups of mice: (1) control mice; (2) GL261-derived tumor-bearing mice mock-vaccinated with phosphate-buffered saline; and (3) GL261-derived tumor-bearing mice vaccinated by subcutaneous (s.c.) injection of irradiated GL261×RAG-neo semi-allogeneic hybrids (10) in PBS . Spleen 6 cells/well in fresh medium (unstimulated) or in a 1:2 dilution of medium conditioned for 24 h by subconfluent cultures of GL261 tumor cells (stimulated). After 3 days of culture, supernatants were collected from the spleen cells and used to measure production of cytokines. Levels of cytokines in GL261-conditioned medium were also measured to control for cytokine levels that may have been contributed by the tumor cells. Levels of cytokines in the culture supernatants were measured using mouse cytometric bead array flex sets according to the instructions of the manufacturer . A FACS Canto (BD Biosciences) flow cytometer was used to quantify cytokine profiles and relative amounts of each cytokine were determined using FCAP Array Software .Spleen cells from each of the groups of mice were cultured in 1 mL in 24-well anti-CD3-coated tissue culture plates at 1 × 10U test was used to determine significance of differences in values between each of two parameters . Significance was reported in the 95% confidence interval. Due to the differences in the number of animals in each group, the Hedges’ g test was used to determine the strength of the effect size as it provides a measure of effect size weighted according to the relative size of each sample.Data were reported as means ± standard error of the mean. The Mann-Whitney The results of these experimental studies show that, for most of the cytokines measured, tumor-conditioned medium stimulated an overall increase in cytokine production by control spleen cells and spleen cells from vaccinated mice. In contrast, spleen cells of tumor-bearing, mock-vaccinated mice produced diminished levels of cytokines in the presence of tumor-conditioned medium. Thus, these results show that the vaccination resulted in a restoration of spleen cell cytokine production in response to mediators produced by tumor cells, as seen by a restored capacity to produce a broad spectrum of cytokines in response to the presence of tumor-derived products.


Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2i–1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors—the synaptic switch for learning and memory—were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques—which preferentially encode specific and low-combinatorial features and project inter-cortically—is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6—which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems—is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain’s basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.There is considerable scientific interest in understanding how cell assemblies—the long-presumed computational motif—are organized so that the brain can generate intelligent cognition and flexible behavior. The  Our knowledge regarding specific genes, neuron types and circuitry functions has expanded substantially since Ramón y Cajal pioneered brain research more than a century ago , whereas the sub-general and general neural cliques categorically extract all possible combinational patterns. Cognitively speaking, specific and sub-general cell cliques encode specific memories or actions for pattern-discrimination and categorization, respectively, whereas higher combinational or generalized neural cliques discover general patterns for pattern-generalization corresponding to semantic memories, categorical knowledge, general intent and motor instruction. In essence, this power-of-two-based permutation logic intrinsically enables each FCM to cover every mathematical possibility of connectivity patterns in a specific-to-general manner Tsien, ,b.i = 4), one should observe all 15 excitatory cell cliques (N1–15) in relevant brain regions that exhibit specific-to-general coding properties.Due to the inherent structure-function relationship, this power-of-two permutation-based wiring logic can be functionally detected by neural recording techniques in the form of specific-to-general cell assembly activation patterns Figure . For exaTheory of Connectivity has offered six testable predictions: (1) Cognitive universality—This power-of-two-based computational logic should be used to process various cognitions across a wider range of modalities—including appetitive, emotional and social information; (2) Anatomical prevalence—This logic should be prevalent across many cortical and subcortical circuits, regardless of their macroscopic and microscopic variations; (3) Modulatory neurons, such as dopamine (DA) neurons, use a different logic; (4) The specific-to-general organization should be developmentally pre-configured, rather than to be formed after learning in adulthood; (5) This computational logic is implemented in the cortex vertically via the differential assignment of specific-to-general cliques to distinct laminar layers. This vertically implemented FCM has the advantage to be readily replicated via horizontal surface expansion ; and (6) Species conservancy—The proposed computational logic is evolutionarily conserved across the brains of different animal species. Here, we describe a series of experiments in testing these six predictions derived from the power-of-two permutation-based computational logic.Importantly, this All animal work described in the study was carried out in accordance with the guidelines established by the National Institutes of Health regarding the care and use of animals for experimental procedures and was approved by the Institutional Animal Care and Use Committee at the Medical College of Georgia at Augusta University  and Banna Biomedical Research Institute of Yunnan Academy of Science and Technology (BBRI#102).Tetrodes and headstages were constructed using the procedures as we have previously described , medial amygdala , anterior cingulate cortex , retrosplenial cortex  and CA1 (n = 5 WT mice) bilaterally with 64 channels per hemisphere . The electrode bundles were positioned above the ILA . For recording in the mouse ventral tegmental area (VTA), we used 64-channel tetrodes with 32 channels targeting each side or a single 32-channel tetrode bundle for a single side .For recording in cortical layers 2 and 3 (L2/3) of the RSC, nine mice were used (eight mice were implanted with 32-channel tetrodes and one mouse with a 64-channel tetrode array). For recording in cortical layers 5 and 6 (L5/6) of RSC, 11 mice were used . For recording from the mouse CA1, the electrode bundles were positioned above the bilateral dorsal hippocampi . To record in the ILA region, we used 64-channel tetrodes with 32 channels per hemisphere  and KO mice (6–8 months old) or adult male hamsters (3–4 months old) were moved from home cages housed in the LAS facility to the holding area next to the chronic recording rooms in the laboratory and stayed in a large plastic bucket  per mouse as their homes with access to water and food for a week prior to surgery. During this period, the animals were also handled daily to minimize the potential stress from human interaction. On the day of the surgery, the animal was given an intraperitoneal injection of 60 mg/kg ketamine  and 4 mg/kg Domitor  prior to the surgery. The head of the animal was secured in a stereotaxic apparatus, and an ocular lubricant was used to cover the eyes. The hair above the surgery sites was removed, and Betadine solution was applied to the surface of the scalp. An incision was then made along the midline of the skull. Hydrogen peroxide  was placed onto the surface of the skull so that bregma could be visualized. The correct positions for implantation were then measured and marked. For fixing the microdrive headstage, four holes for screws  were drilled on the opposing side of the skull and, subsequently, the screws were placed in these holes with reference wires being secured to two of the head screws. Craniotomies for the tetrode arrays were then drilled, and the dura mater was carefully removed. After the electrodes were inserted and tetrodes were secured to the fiberglass base, and the reference wires from the connector-pin arrays were soldered such that there would be a continuous circuit between the ground wires from the head screws and those from the connecter-pin arrays. Finally, the connector-pin array was coated with epoxy. Aluminum foil was used to surround the entire headstage to aid in protection and to reduce noise during recordings. The animals were then awoken with an injection of 2.5 mg/kg Antisedan. The animals were allowed to recover post-surgery at least for 3–5 days before recording began. Then, the electrode bundles targeting the BLA, VTA, MeA, and hippocampal CA1 region were slowly advanced over several days in small daily increments. For the cortical sites, tetrodes were advanced usually only once or twice in a small increment.After surgery, the animals were handled for another 5–10 days while electrodes were advanced to the recording sites for obtaining maximizing neural units. On the day(s) of behavioral experiments, we recorded the neural ensemble activity in freely behaving mice in a clean home bucket for at least 30 min as baseline. For appetitive experiments, various foods  were delivered to the small petri dish located in the clean home bucket to minimize emotional effects due to changes in environment. Seven pellets or milk droplets were delivered for each food type with a 15–30 s time-interval after consumption. The order of delivery was randomized by using different delivery orders for different animals. We noted that mice typically consumed the food within 10–20 s. The time intervals between the different foods were 5–10 min. The recordings were continued for an additional 30 min after all the appetitive experiments were completed. The experiments were videotaped by a camera placed above the recording chamber.For social interaction experiments, we implemented 64-channels in the right hemisphere MeA in four mice and 128-channels bilaterally in the MeA in five mice. These mice were single-cage housed for at least for 1 month before surgery. One week prior to surgery, the mice were transferred to the large home buckets (46-cm in diameter) located in the recording room and maintained single-cage housed and socially isolated until the social interaction experiments. On that day, the recording mouse was first placed in the clean home bucket for 30 min  and recorded as the resting baseline state before a social stimulus mouse  was introduced. To reduce mating behavior, we used anestrus females. Two mice were allowed to freely interact for 10 min and then the stimulus mouse was removed with a 15-min time interval before a second mouse  was introduced. The sex of the stimulus mice was balanced across the trials. Social behaviors during these interactions were videotaped. The most prominent social interactions were the face-to-face interactions and face-to-anogenital interactions. To facilitate peri-event spike raster and histogram analyses, we screened the videotapes and marked social interaction when face-to-face or face-to-anogenital interactions occurred at 1-cm distance as time zero. In some cases, the animals engaged in dynamic interaction from the anogenital area to face or vice versa within 1 s. These trials were excluded from peri-event spike raster analysis to minimize the carry-over effects.For fearful-event stimulation experiments, the animal was introduced to the airpuff chamber (a clean large home bucket), foot-shock chamber, earthquake chamber and drop boxes. Baseline activities were recorded for at least 3–5 min prior to fearful stimulation. Four distinct fearful episodic events were introduced to the mice in a fixed sequence: (1) Airpuff - a sudden airpuff was delivered to the animal’s back  via an air tube; (2) Earthquake-like shake - the mouse was placed in a small chamber (4" diameter and 6"H circular chamber) fixed on top of a vortex mixer and shaken at 300 rpm for 400 ms  prior to surgery insertion, which can then reveal the electrode track under a fluorescent microscope. 4′, 6-diamidino-2-phenylindole (DAPI) staining was used for the counter-staining of the nuclear DNA of the brain cells. Images were collected using a Zeiss 780 Upright Confocal microscope. The stability of the The neuronal activity was recorded by a Plexon multi-channel acquisition processor system , and waveforms were collected using 56 points with 1400 μsec time width. The recorded spike activities from various brain regions were processed in the manner also previously described , and the first derivative of the energy (EnergyD1 in MClust). Automated clustering of excitatory principal cells and inhibitory interneurons was performed by A(after peak). Half-width after trough was defined as the width between the points when the waveform raised to or fell from half-height of the peak. ΔA(after peak) was defined as the area between the waveform and the line segment of peak and the last point of the waveform. The feature ΔA(after peak) could be positive or negative, depending on the shape of the waveform after the peak. The k-means method was employed to achieve automated cell-type clustering, and we found that the vast majority of the units recorded from BLA were putative excitatory principal cells (~86.4%).Because the features PC2 and EnergyD1 provided poor differentiate power in BLA datasets, we employed a novel set of spike waveform features for describing the spike waveforms of neuron firing in BLA, including trough-to-peak width, half-width after trough and the differential integral of area shape ΔFor the optrodes targeting the BLA or VTA bilaterally, a square-shaped arrangement of polyimide tubes per bundle was used for 32 channels/8 tetrodes per hemisphere. For optical fiber, cladding was removed from the two 200-μm core, 037 NA standard, hard-cladding, multimode fiber (ThorLabs), and the optical fibers were placed 1-mm apart in a microdrive base for bilateral targeting of the VTA or BLA using a total of 32 channels/8 tetrodes. Eight tetrodes were threaded into separate polyimide tubes and placed adjacent to each optical fiber to create an optrode bundle with the tip of the optical fibers 600-μm above the recording tetrodes. Tetrodes were then inserted into eight of the polyimide tubes, leaving an empty tube at each corner of the square. This arrangement would allow the tetrodes to be reached effectively by the light-stimulation from the fiber. After each tetrode was correctly positioned, the wires were glued to the polyimide tubes. The free ends of each tetrode were wrapped around the 36 pin-connector array and were individually soldered to their respective pins. The recording tips of the tetrodes were cut so that they would protrude past the fiber 300–500 μm.Prior to stimulation, PM100D (ThorLabs) was used to measure the light intensity. Optical fibers were connected to a blue laser . Trains of 10-Hz stimulations  were delivered to the site in 10 trials using a PulsemasterA300. To confirm the identity of pyramidal cells that were based on waveform characteristics, we recorded from the BLA of two double transgenic mice (Tg-CaMKII-Cre crossed with Tg-Ai32-ChR2). To verify DA neurons, we used a combination of pharmacological and optogenetic methods that we previously published / Here, fresp, i represents the average firing rate during the detected neuronal response after the stimuli i. fpre is the average firing rate during baseline before the stimuli i, and f0 is the averaged basal firing rates of all isolated units in a given region . Note that this transformation allows for uniform quantification of the significant changes in firing patterns for units with both low- and high-baseline firing rates.To determine whether a recorded unit was responsive to a given stimulus, we used the stimulus time points as time zeros to calculate a peri-event histogram using a 500 ms-bin size for appetitive stimulation, a 200 ms-bin size for social interaction, and a 100 ms-bin size for fearful stimuli. The neural activities before stimulus were used as a baseline to determine confidence intervals . To assess significant changes in firing, we used 95%, 99.9%, 99.999%, confident intervals and the duration longer than five bins, three bins, and two bins for a given category of stimulation, respectively, then it is considered a significant neuronal response to the stimulus. These criteria are determined empirically. To facilitate the comparison between units that exhibit different increases/decreases over baseline activities, we used the transformation Z-score transformation. Z-score values were calculated by subtracting the average baseline firing rate established over the defined duration preceding stimulus onset from individual raw values then by dividing the difference by the baseline standard deviation.For measuring the population response significance, neural activity was calculated by comparing the firing rate after stimulus onset  with the firing rate recorded during the baseline periods  using a T = log(1+|R|). Here, R is a n × m matrix representing the neuronal responses of n units during m stimulus, and | | denotes the absolute value. An agglomerative hierarchical cluster tree was created from the standardized Euclidean distances. Then, a categorical sorting was applied to facilitate the visualization. That is, units were sorted by the number of stimuli to which these units responded. After the categorical sorting, the units which were non-responsive were put on the top of the matrix, followed by the units responding to a specific event, and the units responding generally to the most number of types of stimulus events were located at the bottom of the matrix.Hierarchical clustering methods were used to investigate the stimulus responses of the overall population of the simultaneously recorded units. The procedure was similar to the one previously described , where i goes from 1–16, and j from 1–4, and Ej can be 0 or 1. Example histogram for ACC WT mice:In order to test if the response patterns of the recorded ensembles deviated from random distribution, we constructed a null (independent random) model which assumed that each neuron in the given region has an independent response probability to a given pattern, and the response probabilities of this neuron to different stimuli can be different. In other words, each event would randomly activate a subset of the neurons with a different average ensemble size, and the activation patterns of different stimuli are independently chosen and do not interact. Accordingly, we normalized the observed histogram of counts for 16 clique response-types to four different stimuli (example shown below) by the total number of counts to obtain the response pattern distributions denoted as #1 clique : 114 cells (non-responsive units)#2 clique : 35 cells (responded to the first stimulus)#3 clique : 25 cells (responded to the second stimulus)#4 clique : 13 cells (responded to the third stimulus)#5 clique : 35 cells (responded to the fourth stimulus)#6 clique : 23 cells (responded to the first and second stimuli)#7 clique : 15 cells (responded to the first and third stimuli)#8 clique : 13 cells (responded to the second and third stimuli)#9 clique : 21 cells (responded to the first and fourth stimuli)#10 clique : 13 cells (responded to the second and fourth stimuli)#11 clique : 14 cells (responded to the third and fourth stimuli)#12 clique : 37 cells #13 clique : 19 cells #14 clique : 17 cells #15 clique : 31 cells #16 clique : 95 cells ip  = p(E1 = 0) × p(E2 = 1) × p(E3 = 0) × p(E4 = 0). The independent probabilities can be obtained by summing all pis where a given event occurs. For example, p(E1 = 0) can be obtained by summing all pis with E1 = 0, regardless of the value of the other variables. To generate the error bars, we used a resampling procedure by running 1000 Monte-Carlo simulations. In each simulation, we assigned each data point to one of those 16-clique patterns by drawing a random number weighted by the probability of the occurrence of each pattern. The 5% and 95% values were plotted on the graphs, and the p value for the probability of a given pattern being significantly different from the actual distribution was assessed at a level of p < 0.05 after dividing the p value by 16 to reflect the Bonferroni multiple hypothesis testing correction.The probability of generating the combined pattern is the product of individual independent probabilities, i.e., in vivo tetrode arrays . To avoid possible training-induced changes in coding patterns, we only used the datasets collected from the very first food-encounter experiments during which the mice consumed seven sugar pellets, seven rice and seven condensed-milk droplets for the first time, in addition to seven rodent diet pellets .We employed large-scale A total of 794 units were recorded from the BLA in nine WT mice, and 527 putative pyramidal units met the above criteria and were used for peri-event spike raster histogram analysis. Interestingly, we found that putative pyramidal cells exhibited a variety of event-response selectivity, including the general-responsive cells that increased their firings to all four types of foods, the combination of two or three types of appetitive stimuli, or those specific units responding only to one type of food Figure . The divN = 24− 1 = 15) which covered every mathematical possibility required to process four distinct food experiences  increased firings to rodent diet . Of them, 602 units met the criteria to be well-isolated. These units were then separated into putative excitatory units and fast-spiking interneurons . Stable units before, during and after fearful stimulation were selected for further analysis , and 520 putative excitatory units were identified. Many ACC excitatory cells (406 units) increased their firings to these fearful stimulations in a specific-to-general manner  known to trigger emotional responses , RSC and CA1 region of the hippocampus , and 296 units were judged as stable and well-isolated. Of them, 205 units were classified as putative excitatory units . Our large-scale recordings again detected 15 distinct neural cliques (from 148 responsive units) showing the specific-to-general coding patterns . Our analyses of these pyramidal cells (264 responsive units) in pooled data also uncovered 15 specific-to-general response types  revealed that the power-of-two-based permutation logic was, indeed, repeatedly employed across various network stages of the emotional memory circuits.n = 10 mice) based on a combination of criteria—their waveforms  and decreased firing upon injection of D2 agonist apomorphine . In a subset of experiments, DA units were further confirmed by the optogenetic method . We noted that DA neurons can be classified into three major types. Type-1 and -2 DA neurons suppressed their firings during fearful stimulation, and these neuron DA subtypes (n = 27 units) were broadly tuned to these fearful stimulations  exhibited significant rebound-excitation at the termination of fearful stimulation , whereas Type-2 DA neuron (n = 3 units) lacked such rebound-excitation .The third prediction is that because the modulatory neurons—such as DA neurons in the VTA - have different functional purposes . Accordingly, they may use a simpler logic. We recorded from the VTA in freely behaving mice Figures  while al Figures . Type-1 In contrast to the suppressed firing upon fearful stimulation, Type-3 DA neurons (nine units recorded) increased firing to air-puff, free-fall, earthquake or foot-shock Figure , and theTheory of Connectivity predicts that this power-of-two-based logic should be pre-configured by evolution and development. In other words, this logic should be independent of learning in adulthood. To examine these two competing hypotheses, we deleted the N-methyl-D-aspartate (NMDA) receptors in the forebrain excitatory neurons postnatally starting at 6-weeks’ old . A total of 659 putative pyramidal units were identified from seven KO datasets. Of them, 279 cells responded to these four distinct foods. Fifteen distinct specific-to-general neural cliques were found among these pyramidal cells to represent these four food experiences in a surprisingly similar way to that of WT mice Figure .n = 6) and control mice (n = 6) while they were exposed to four distinct fearful stimuli . We obtained a total of 295 putative excitatory units from the RSC of the KO mice and 326 units from the control mice. Of them, 175 units out of 295 cells in the KO mice and 165 units out of 326 WT cells reacted to these fearful stimuli. Once again, we also identified 15 specific-to-general neural cliques in the RSC of both the KO mice and WT littermates  and control mice (n = 3) while exposing them to air-puff, acoustic startle, earthquake and free-fall. A total of 249 or 302 putative excitatory cells were identified from the KO and WT ACC region, respectively. 225 units out of those 249 cells in the KO mice and 213 units out of those 302 cells responded to these fearful events. There were 15 distinct specific-to-general cliques in both the KO datasets and their WT datasets , whereas general clique cells should be concentrated in the deep layers five and six  while the same four types of fearful events were delivered. Hierarchical clustering analysis of 168 putative excitatory cells showed that the percentage of cells belonging to one-event specific cells was significantly smaller  contained 15 specific-to-general cell assemblies is consistent with the notion that this power-of-two-based permutation logic serves as the general computational algorithm of the cortex.Over evolution, different animals have developed a variety of drastically different sensory apparatus , thereby leading to higher abstraction of categorical knowledge and more intelligent behaviors. Over time, when the random connectivity strategy may no longer be sufficient and efficient to ensure the desired outcome, evolution exerts its selection force to develop nonrandom organization to execute this power-of-two-based permutation logic to efficiently deal with environments in which animals lived. This is supported by our findings that specific-to-general cell assembly patterns in the CA1, BLA and cortex (combined from L2–6) all differed significantly from the independent random-connectivity model.In the early stages of evolution when animals began to appear 500–600 million years ago, the random connectivity strategy may be initially used to execute this power-of-two permutation-based mathematical logic by constructing a simple circuitry node for rudimentary pattern-separation and pattern-generalization Tsien, ,b. For eInterestingly, within the cortical architecture, our analysis suggests that evolution employed both random and nonrandom connectivity strategies to construct its laminar layers—namely, randomness for L2/3 organization vs. nonrandomness for L5/6 organization. Once this cortical FCM was in place Figure . The horAt the cognitive level, the specific-to-general cell-assembly logic offers the network-level explanation as to how the brain can encode not only specific memories but also categorical knowledge  or a category of items ,  can quickly become prohibitive. For instance, in order to cover all possible patterns for processing 40 distinct perceptual inputs, a single FCM would require over a trillion neurons (1012). Even neurons in the human brain (8.6 × 1010 neurons) would be inadequate to afford this exponential coverage if this power-of-two-based logic were to be rigidly applied. The best and necessary solution is to employ modular approaches, or a divide-and-conquer strategy, to segregate or stream information inputs through distinct sensory domains or pathways.While the power-of-two-based permutation logic is based on the mathematical equation of N = 28 −1 = 255) for this node. However, when a sub-modular approach is employed , the same 255 principal neuron sets can increase its processing capacity by a factor of 17 times . Similarly, if a subnode or FCM was structured to process only three distinct pieces of information (N = 23 −1 = 7), 255 neurons can be used to construct 36 assemblies for processing 108 distinct information inputs. Through evolution, i number should have been selected and confined by the complexity of given environmental demands in which organisms have lived for generations. This means that evolution has been forced by this mathematical cost-and-benefit analysis to use neuron resources efficiently and wisely, as evident from the evolutionarily conserved specific sensory pathways and cortical modalities. This boundary condition can be tested in simple organisms such as drosophila larvae or C. elegans as well as classic mammalian models such as mice and rats by increasing numbers of inputs. Perhaps it should also be explored and expanded in computational modeling, neuromorphic chips and artificial intelligence systems.For example, if a central node in a small neural circuit needs to cover all possible connectivity wiring patterns to represent eight distinct types of inputs or information, a total of 255 principal projection neurons would be required  or to a tail-pinch or foot-shock in anesthetized rats , the One of the key questions is whether this power-of-two-based, cell-assembly logic should be formed by learning in adulthood or pre-configured by development, or both. We employed two approaches to examine the role of learning in our current experiments: one was to take steps to minimize learning in the present experiments, and another was to delete the NMDA receptors in the forebrain right after postnatal development.In our experiments, various specific stimuli  were given for the first time to the recorded mice whenever possible . Our datasets were all collected in the very first set of trials . This ensured the patterns were not a result of repeated conditioning.We then used forebrain NMDA receptor KO mice to assess whether learning is necessary to the observed coding logic. It is known that synaptic proteins are metabolically turned over within hours, days or week(s). This means that learning-induced synaptic connectivity should drift significantly over time  in which structural connectivity and functional imaging can be better analyzed  and KO mice; JL generated the datasets from the BLA in WT and KO mice; SJ and HK for the IL datasets; HK and JL for the CA1 datasets; JCL for the DA neuron datasets. CL, ML, HK and SS for data analyses together with KX, GEF, JL, JCL, TL and JZT. JZT wrote the article with input from all the other authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background. The aim of this study was to compare the push-out bond strengths of calcium silicate-based ProRoot MTA and Biodentine cements and SureFil SDR and EverX Posterior bulk-fill composite resins.Methods. Twenty-four single-rooted maxillary central incisors were sectioned below the cementoenamel junction, and the root canals were instrumented using rotary files. Thereafter, a parallel post drill was used to obtain a standardized root canal dimension. The roots were randomly assigned to one of the following groups with respect to the intra-orifice barrier used: ProRoot MTA; Biodentine; SureFil SDR; EverX Posterior. Five 1-mm-thick sections were obtained from the coronal aspect of each root. Push-out bond strength testing was performed and data were analyzed with Kruskal-Wallis and post hoc Dunn tests (P<0.05).Results. SureFil SDR and EverX Posterior bulk-fill composite resins’ bond strengths were significantly higher than ProRoot MTA and Biodentine calcium silicate cements. However, no statistically significant differences were observed between bulk-fill composite resins values and calcium silicate cement values.Conclusion. Within the limitations of present study, calcium silicate-based ProRoot MTA cement’s push-out bond strength was lower than those of Biodentine, SureFil SDR and EverX Posterior materials. For this purpose, it has been suggested that to prevent the penetration of oral fluids and microorganisms into the root canals 3‒4 mm of coronal gutta-percha should be removed from the root canal and an intra-orifice barrier should be placed at canal orifice3 or a pulpal base should be placed using a restorative material.4 Previous studies have reported that covering the pulpal base with intra-orifice barrier materials after the root canal treatment constructs a secondary defense line against bacterial leakage.5 For this purpose, different materials have been employed, including temporary filling materials, glass-ionomer cement, composite resin, MTA and IRM.7Coronal leakage is one of the most important reasons for the failure after root canal treatment.9 MTA has been shown to be a bioactive material inducing the formation of hard tissues; it is well-tolerated by the tissues it contacts because of its biocompatibili.y11 However, long setting time of MTA (4‒6 hours) and the difficulties in adjusting the consistency while mixing make this material non-practical for clinical use.12 Numerous materials based on tri-calcium silicate have been developed and introduced to the market in order to eliminate these disadvantages of MTA.14 Biodentine  is a new endodontic cement containing tri-calcium silicate and calcium carbonate, with a setting time of 12 minutes.15 The manufacturer claims that BD can be used as a replacement for dentin tissue for restorative purposes and as direct pulp cupping material for endodontic purposes, as well as restoration of perforations and as a root-end filling material.16Today, MTA is also safely used in conservative pulpal treatments, root resorption treatments, and apexification procedures.18 Another fiber-reinforced bulk-fill composite resin, also newly introduced to the market, is EverX Posterior .SureFil SDR flow , one of the bulk-fill composite resins recently introduced to the market, is a silorane-based nano- and micro-hybrid composite with low viscosity; its shrinkage stress is lower than conventional fluids.In comprehensive literature research, no study was found, comparing the push-out bond strengths of SDR and EXP bulk-fill composite resins. For this reason, the aim of the present study was to compare the push-out bond strengths of calcium silicate-based ProRoot MTA and BD cements and SDR and EXP bulk-fill composite resins. The null hypothesis of the present study was there would be no statistically significant difference between the push-out bond strengths of the tested materials.19 maxillary central incisors, with no signs of calcification and extracted due to periodontal reasons, were included in the present study. The teeth were examined under ×2.5 magnification, and those with fractures or cracks or multiple apical foramina were excluded and replaced with new ones. In order to ensure the standardization, the crowns of the teeth were removed (by ensuring 16 mm of root length) with a fine diamond disc  at cementoenamel junction perpendicularly to the long axis of the teeth under water-cooling.Twenty-four single-rooted (0‒5°)-1 torque values in DR’S CHOICE program of VDW Reciproc Gold  endodontic motor. Each of the new file sets was used for shaping 4 canals and discarded. After each file, the root canals were irrigated with 2 mL of 5.25% NaOCl solution. Then, by using a parallel post drill with a diameter of 1.25 mm , 10-mm-length gaps were prepared within the root canals. For final irrigation of the root canals, 2 mL of 17% EDTA  for 2 minutes and then 2 mL of 5% NaOCl for 2 minutes and 5 mL of distilled water were utilized.Under ×2.5 magnification, the root canals of the teeth were penetrated using a #15 K-file ; the file was inserted until it could be seen at the apical foramen, and then the working length was set at 1 mm shorter than this length. In order to shape the root canals, ProTaper Next  rotary file system’s X1, X2, X3 and X4 files were used respectively. The files were used at 300 rpm and 200 g cmFive 1-mm-thick transverse slices were taken under water-cooling  in corono-apical direction from each tooth. The slices were randomly divided into 4 groups (n=30). In the ProRoot MTA and BD groups, the materials were prepared in accordance with the instructions of manufacturer. The materials were placed on the dentin slices on a glass slab by using a hand plugger (Dentsply Maillefer), and the residual material was removed using a plastic spatula. In the SDR and EXP groups, the canals of the dentin slices were etched for 15 seconds using 35% phosphoric acid , rinsed for 15 seconds and air-dried under low-level pressure of air (left in moist form). Two-step etch-and-rinse adhesive Prime & Bond NT (Dentsply DeTrey) was applied and kept for 20 seconds, and then the canals were dried with low-level pressure of air for 5 seconds and light-cured  for 10 seconds. Dentin discs were filled using SureFil SDR and EverX Posterior on the glass slab, and then light-cured for 40 seconds (Elipar S10). All the prepared samples were kept at 37°C and 100% humidity for 7 days. The particulars of the tested materials are presented in After the samples set completely, each slice was fixed on a steel base with a hole in its center, and then connected to a universal test machine  . For pus2)Bond strength (MPa) = Force for dislodgement (N) / Bonded surface area Following the push-out test, the slices were examined under a stereomicroscope at ×40 magnification to determine the nature of bond failure. Each sample was categorized into one of the three failure modes: adhesive failure at dentin‒material interface, cohesive failure within the material, or mixed failure, which is the combination of the two failure modes . The opeThe normality of data distribution was determined using Shapiro-Wilk test. Then the statistical differences between the groups were calculated using Kruskal-Wallis and post hoc Dunn tests. All the analyzes were performed using SPPS 21  software, and the statistical significance was set at 5%.The mean and standard deviation values of the tested materials obtained from the push-out bond strength test are presented in The frequencies of fracture types in the test materials are presented in 22 For this purpose, in the present study, the push-out bond strengths of ProRoot MTA, BD, SDR and EXP materials were compared.The cements used for endodontic purposes must tightly bind to the root canal walls and resist tooth movements or mechanic stresses that may occur during treatment procedures.23 Push-out test has been reported to be a reliable and practical test for examining the bond between materials and root dentin. In this test method, similar to the clinical environment, the fractures occur parallel to the resin‒dentin bond surface, and this method offers a better opportunity for analysis compared to the traditional shear test.24 For this reason, the push-out test was employed in the present study. The reason for obtaining 1-mm-thick dentin slices in the present study was that frictions that may cause misinterpretation of the results may occur in push-out test, and that it would be more reliable to utilize 1-mm-thick slices in order to minimize friction to eliminate this risk.25The bond of endodontic materials to the root dentin can be tested using different test methods such as traditional shear and push-out tests.17 a peculiar shrinkage behavior of the new flowable material in comparison with other flowable, nano- and micro-hybrid composites and a silorane-based material, was observed. The authors reported lower shrinkage stress, delayed point of gelation and lower shrinkage stress rates for SDR compared to the other materials that have been investigated. The high push-out bond strength of EXP material might be attributed to the ability of distribution of stresses occurring on the fibers distributed throughout the composite’s matrix.26According to the results of the present study, the push-out bond strength values of SDR, EXP and BD groups were significantly higher than those in the ProRoot MTA group. Therefore, the null hypothesis of the present study was rejected. Since there is no previous push-out bond strength study carried out using SDR and EXP bulk-fill composite resin in the literature, the results of the present study cannot be directly compared with other studies. However, the favorable outcomes of SDR might possibly be explained by its favorable stress behavior. In a recent study,30 Moreover, Silva et al31 and Centenaro et al32 reported that BD had significantly higher push-out bond strength than MTA Angelus . In another study, Alsubait et al33 examined the push-out bond strengths of white MTA , BD and Bio Aggregate  cements and reported no significant difference between MTA and BD. The reason for BD’s higher push-out bond strength compared to ProRoot MTA might be small molecular volume of BD cement and better penetration of cement into dentinal tubules, and consequently increased strength of bond to dentin. Moreover, because of this effect, the crystal formation might construct a dentin bridge within the dentinal tubules and thus the cement’s mechanic retention might increase.34Similar to the present results, it has been reported in many studies that BD has significantly higher push-out bond strength values than ProRoot MTA.32 reported higher incidence of mixed-type fracture after they examined the fracture modes of MTA Angelus and BD cements after push-out test. However, the incidence of adhesive failure has been reported to be higher in other studies.36 These differences in the results might be explained by differences in methodologies used in sampling and preparation.According to the results of present study, the fracture modes of the samples generally were mixed. The good bond of materials to dentin, because of the high push-out bond strength values exhibited by BD, SDR and EXP groups, might explain this result. Similar to the results of the present study, Centenaro et alWithin the limitations of the present study, it might be concluded that calcium silicate-based ProRoot MTA cement’s push-out bond strength was significantly lower than those of BD, SDR and EXP materials, with no differences between the push-out bond strength values of BD, SDR and EXP.None.The authors declare no competing interests with regards to the authorship and/or publication of this article.The study protocol was approved by Ondokuz Mayis University ethics committee.


Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations. The speed at which we make perceptual decisions varies. This translation of sensory information into perceptual decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural-network embodiment supporting perceptual decision-making is unclear. We addressed this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, we traced the large-scale network dynamics of coupled neural oscillations to uncover the brain network states that support the speed of auditory perceptual decisions. We found that higher network segregation of coupled beta-band oscillations supports faster auditory perceptual decisions over trials. Moreover, when auditory perceptual decisions are relatively difficult, the decision speed benefits from higher segregation of frontal cortical areas, but lower segregation and greater integration of auditory cortical areas. Contemporary neuroscience is departing from a focus solely on regional brain activations, toward emphasizing the network organization of brain function. This view recognizes the large-scale interactions between distributed cortical areas as the biological basis of behavior and cognition . A more Accumulating evidence suggests that frequency-specific neural oscillations are key to processing sensory information . For exaSpecifically, we asked how the network Naturally, the cortical networks involved in processing sensory information require context-sensitive configurations, as well as moment-to-moment reconfigurations to fulfill dynamic task adjustments . This leTherefore, to find the neural network substrate of auditory perceptual decision-making, we adopted the framework of dynamic brain networks  and mergWe applied this approach to MEG signals measured from human listeners who made perceptual decisions on brief SEMs: pitch task = 76% ± 1.1, direction task = 70% ± 2.1; mean decision speed [s−1] ± SEMs: pitch task = 1.8 ± 0.1, direction task = 1.7 ± 0.1). The bootstrap Kolmogorov–Smirnov test revealed that the distributions of the behavioral measures did not significantly deviate from a normal distribution . Despite our experimental efforts to equate the difficulties of both tasks, analysis of variance (ANOVA) revealed main effects of task for both accuracy [F = 6, p = 0.024, F = 9.9, p = 0.005, p = 0.02). In addition, the decision speed was significantly lower in the direction task than in the pitch task (p = 0.004). The experimental manipulation of the pitch of the acoustic textures yielded significant effects of stimulus spectral center on both accuracy [F = 64, p = 2e−16, F = 30, p = 9e−12, F = 45.3, p = 4e−15, ρ = 0.9, p = 5e−6). However, the correlation between the accuracies in the two tasks was not significant .The participants judged the overall pitch or sweep direction of the acoustic texture stimuli and showed accuracies around 70%, as we had intended from use of the adaptive-tracking procedure . The trial-by-trial estimates of decision accuracy in the direction task also showed a significant positive correlation with the coherence of the stimuli . A significant negative correlation emerged between decision accuracy in the pitch task and stimulus coherence .The correlation between the trial-by-trial estimates of decision accuracy or speed and the trial-by-trial acoustic features of the stimuli was tested using a two-level regression analysis see . We founWe investigated power perturbations in the MEG oscillatory signal while subjects listened to the acoustic textures and judged either their overall pitch or sweep direction. As is illustrated in Since the aim of this study was to find the relation between frequency-specific brain network states and auditory perceptual decision-making on a trial-by-trial basis, we next focused on trial-by-trial fluctuations in the power of the source-projected signals. Toward this end, we implemented a general linear model (GLM) per participant, whereby the time series estimates of trial-by-trial decision accuracy or speed  were preThe aim of this study was to find the frequency-specific brain network states underlying individuals’ perceptual decision-making in the course of judging auditory stimuli. The auditory stimuli were identical but were presented in two distinct task sets—that is, either judging the overall pitch or the overall direction of frequency-modulated tone sweeps. To predict trial-by-trial decision-making performance from the ongoing brain network states, we implemented a linear regression model in which the time series estimates of trial-by-trial decision accuracy or speed  were preOn the whole-brain level, for both the pitch and direction tasks, we found significant cor relations between decision speed, on the one hand, and the functional connectivity and topology of dynamic brain networks, on the other hand . These cMore specifically, we found positive correlations between the mean functional connectivity of dynamic brain networks and decision speed in both the pitch and direction task within the frequency range of 16 to 28 Hz  or the poststimulus interval (0 to +1 s) separately. In addition, to examine the extent to which our results might merely reflect neural processes involved in giving manual responses after the response prompt see , we alsoTo test the task specificity of the correlations between a given network diagnostic and trial-by-trial decision accuracy or speed, we also computed mean differences in the regression weights. We found no significant difference between the two tasks in predicting decision accuracy or speed from the ongoing dynamics of network topology on the whole-brain level. In addition, to investigate the possible lead/lag relationship between brain network states and trial-by-trial decision-making performance, we computed the cross-correlations between behavioral time series, on the one hand, and the dynamics of brain networks, on the other hand. This analysis replicated significant neurobehavioral correlations that peaked at a zero trial lag  by means of separate linear regression models . In each model, we treated the trial-by-trial acoustic features as the dependent variable and tested the significance of the mean regression weights averaged over subjects. On the whole-brain level, we did not observe any consistent significant correlation between brain network metrics and acoustic features in either of the tasks  of neural oscillations and were only observed when neural oscillatory responses within both the pre- and poststimulus intervals were used to estimate the trial-by-trial brain network states.The participants judged identical acoustic stimuli under two distinct task sets. Therefore, we not only expected similar network states to correlate with auditory perceptual decision-making in both tasks , but also anticipated finding task-specific network states . On the whole-brain level, we found no significant difference between the two tasks in predicting the trial-by-trial speed or accuracy of auditory perceptual decisions from the ongoing dynamics of brain networks.However, the regional properties of large-scale brain networks could still predict decision speed specifically in either the pitch or the direction task, or in both, but in different directions. Thus, we aimed to investigate the regional network states that would differentially predict the speed of auditory perceptual decisions during the pitch versus the direction task.nodal strength), (B) local efficiency, (C) modular segregation , and (D) nodal efficiency.connectivity of two network nodes  located within the left and right temporal gyri showed significant differential correlations with decision speed during the pitch task in contrast to the direction task  converged on decreases in the nodal connectivity, local network efficiency, modular segregation, and nodal network efficiency of regions within auditory cortex. Within the frontal cortex, however, faster decisions during the direction task were predicted by increases in local network efficiency and modular segregation.Taken together, the results obtained at the regional level of whole-brain networks point to stronger correlations between brain network states and the speed of perceptual decisions during the direction than during the pitch task. These predictions emerged from auditory and frontal cortices. Among these predictions, the stronger ones  nodal connectivity, (ii) local efficiency, (iii) modular segregation, and (iv) nodal efficiency of source locations mostly overlapping with the anterior division of left superior temporal cortex. Within frontal cortex, however, increases in (i) the local efficiency of left middle and medial frontal gyri and (ii) the modular segregation of right anterior cingulate predicted faster decisions during the direction task.Our task-differential analyses at the regional level suggest that the arguably more difficult direction task, in comparison to the pitch task, relied critically on specific network configurations of beta-band oscillations. The differences we found in nodal network topology across the two tasks were specific to MEG sources located within the temporal and frontal cortices. Within the vicinity of auditory cortex, effects in support of faster decisions during the direction task converged toward One pattern forged of these results is that faster decisions during a particularly challenging auditory perceptual task are accompanied by an increase in the network segregation of frontal regions, and that this segregation supports higher-order decision-related processes. Theoretically, high local clustering of neighbor nodes is associated with high efficiency in local information transfer and fault tolerance , indicatThe critical involvement of frontal regions in perceptual decision-making has been supported by previous animal studies on local field potentials (LFPs), in which frontal cortex responses were found to selectively encode auditory stimulus features  or to shIn contrast to what we observed in frontal regions, network states concurrent with less clustered and less segregated auditory regions were found to speed up decisions during the direction task. One possibility is that during these states brain networks were more integrated (the opposite pole of segregation). However, we did not find significant correlations between decision speed and so-called module participation , a well-We also note that faster decisions during the direction task were predicted by a decrease in the nodal efficiency of left auditory regions. This was perhaps due to the emergence of longer paths between these regions and other network nodes and the pruning of long-range shortcuts. This globally less-efficient and more-distributed information routing might be necessary to support perceptual decisions for the more difficult direction task. Indeed, a study by To conclude, the present study suggests that the large-scale network organization of coupled neural oscillations at ∼20 Hz (the beta band) underlies how quickly momentary auditory perceptual decisions are made and executed. Thus, global communication in brain networks during perceptual decision-making is likely implemented by neural oscillations at around 20 Hz. During auditory perceptual decision-making, this dynamic global communication appears as complex network interactions between beta-band neural oscillations evolving within lower-order auditory and higher-order control areas.SD age = 26.2 ± 3.35 years) participated in the study. None of the participants reported any neurological diseases or hearing problems. Ethical approval was obtained from the local ethics committee of the University of Leipzig. All procedures were carried out with written informed consent of the participants and in accordance with the principles of the Declaration of Helsinki. Volunteers received monetary compensation for participating in the study.Twenty healthy, right-handed volunteers , the acoustic textures had one of eight spectral centers relative to a mean center frequency of 707.1 Hz. More specifically, the textures could deviate ±2, 1, 0.5, or 0.25 semitones from the center frequency. Their spectral centers were approximately 630, 667, 687, 697, 717, 728, 749, and 794 Hz.For manipulation of the spectral coherence, a variable proportion  of the sweeps were assigned the same frequency slope; that is, they were “coherent” with one another. The rest of the sweeps had randomly assigned slopes.The participants had to judge one feature of the auditory stimuli under two distinct task sets . The feaTrials started with a white fixation cross that appeared on a black background at the center of a back-projection screen. After a prestimulus interval of 1 s (with no time jitter), an acoustic texture was presented. Then the participants gave a delayed response to the auditory stimulus. This means that 0.6 s after the offset of the acoustic texture, the participants were visually prompted by the decision labels for the left- and right-hand response buttons see . During To prevent systematic effects of motor preparation, the mapping of the high/low and up/down labels to the two response buttons was randomized across trials. Participants had 3 s at maximum to respond before the experiment would automatically proceed to the next trial. As in all MEG studies from the Leipzig Max Planck center e.g., , the audOn a separate session before MEG recording, adaptive perceptual tracking was conducted with feedback so that each participant’s average accuracy converged at ∼70% in each task. This was done to avoid ceiling or floor effects, and to assure that the two tasks were challenging enough.pitchtask and the directiontask, alternated from block to block, and the initial task was randomized across the participants. Each block contained two trials of each possible combination of direction (two levels), coherence (four levels), and spectral center (eight levels). Subjects were presented two blocks at the beginning to familiarize them with the stimuli and the tasks. The last six blocks were used for data acquisition, resulting in a total of 384 trials per task for each subject. Between the blocks, participants were given self-paced breaks.At the recording session, the participants completed eight task blocks, each consisting of 128 trials (∼8 min). The two auditory paradigms, namely the −1). Decision speed was calculated relative to the onset of the response prompt  and decision speed . The choice of the window size was guided by a recent study by z-scored) before estimating the regression models  of the auditory stimuli, we used a two-level general linear model (GLM). In this model, the time series estimates of decision accuracy or speed were predicted by an acoustic feature. Since the positively skewed distribution of response times can violate the normality assumption underlying the general linear model , the depn models .MEG responses were recorded using a 306-channel Neuromag Vectorview system  in an electromagnetically shielded room  at a sampling rate of 1,000 Hz with a bandwidth of 330 Hz DC. Movement of each participant’s head relative to the MEG sensors was monitored by means of five head-position measurement coils. An electroencephalogram from 64 scalp electrodes (Ag/AgCl) was recorded but not analyzed in this study.The raw MEG data were first subjected to the Maxfilter software to suppress any disturbing magnetic interference using the signal space separation method . Next, tThe signals were high-pass-filtered at 0.5 Hz  and low-pass-filtered at 140 Hz . Next, epochs from −1 to +2 s around the onset of the acoustic textures were extracted and down-sampled to 500 Hz.SEM: 29.4 ± 4).Independent component analysis was employed to exclude artifactual components containing heart, eye, or muscle activity. Then the single-trial sensor data were visually inspected to exclude trials that still contained eye blinks or movements, muscle activity, static or flat intervals, signal jumps, or drifts, or that had a range larger than 300 pT/m , at frequencies ranging from 8 to 32 Hz on a logarithmic scale . The logarithms of the squared magnitudes of the wavelet coefficients were then baseline-corrected relative to the power of the signals within −0.5 to 0 s.http://martinos.org/mne). The fit of approximately 200 digitized head surface points (Polhemus Fastrak 3D digitizer) to the reconstructed head surface was optimized using the iterative closest-point algorithm after the manual identification of anatomical landmarks . The individual segmented and coregistered anatomical images were spatially normalized to the standard stereotaxic Montreal Neurological Institute space. The inverses of these operations were applied to a 12-mm grid created in the template brain in order to obtain subject-specific grids in the standard space .Individual forward head models were created on the basis of each participant’s T1-weighted magnetic resonance image . The anatomical images were segmented using Freesurfer and coregistered to the MEG coordinates using the MNE software , in steps of 0.05 s, were used to extract complex-valued spectrotemporal estimates of the source-projected signals per trial (41 data points). For analysis of the prestimulus interval, time points from −0.85 to 0 s (relative to the onset of the acoustic textures) were used. For analysis of the poststimulus interval, we analyzed the time points from the onset of the acoustic textures up to +1 s, during which the participants listened to the auditory stimuli but did not manually give their responses. Additionally, since the power of beta-band oscillations is known to be related to the preparation and execution of movements , we perfz-scored) before estimating the regression models  per subject and trial, for each pitch and direction task  was used to measure the strength of the membership of a given source in its network module. Finally, nodal efficiency was measured to capture the integration of a given source into the entire network  before estimating the regression model .To measure the task specificity of the correlations between a given network diagnostic and either trial-by-trial decision accuracy or speed, we also computed the mean difference between the regression weights ρ correlation  for repeated measures, using Task , Coherence (four levels), and Spectral Center (four levels) as within-subjects factors. We used generalized eta-squared ηG2;  as the erelation  with 10,relation . To testTo predict trial-by-trial perceptual decision accuracy or speed from the ongoing brain oscillatory power or network states, we implemented a two-level GLM approach. The regression weights obtained from the fit of each GLM were averaged over participants at each frequency and statistically compared with a null distribution. The null distribution was generated using a randomization procedure in which the trial-by-trial binary responses or decision speeds were circularly shifted 350 times  over trials, per task and per subject. Circular shifting preserves the autocorrelation structure inherent to the time series  as the threshold of significance. For the analysis on the whole-brain level, in order to correct for the multiple comparisons entailed by the number of frequency bins (14), we implemented a correction method suggested by Fs is the number of selected frequencies at the first pass, Ft is the total number of frequency bins tested, and q is the tolerated rate for false coverage statements, here 0.05. The FCR correction procedure yields inflated, and thus more conservative confidence limits  we used This research was supported by the Max Planck Society (through a Max Planck Research Group grant to J.O.) and the European Research Council . Yvonne Wolff and Burkhard Maess helped acquire the MEG data.Mohsen Alavash: Conceptualization; Formal analysis; Investigation; Methodology; Visualization; Writing – original draft; Writing – review & editing Christoph Daube: Data curation; Formal analysis; Visualization; Writing – review & editing Malte Wöstmann: Conceptualization; Methodology; Writing – review & editing Alex Brandmeyer: Conceptualization; Data curation; Methodology; Visualization; Writing – review & editing Jonas Obleser: Conceptualization; Funding acquisition; Methodology; Project administration; Resources; Supervision; Writing – review & editing


Postprandial hyperinsulinemia, hyperglycemia, and insulin resistance increase the risk of type 2 diabetes (T2D) and cardiovascular disease mortality. Postprandial hyperinsulinemia and hyperglycemia also occur in metabolically healthy subjects consuming high-carbohydrate diets particularly after evening meals and when carbohydrate loads follow acute exercise. We hypothesized the involvement of dietary carbohydrate load, especially when timed after exercise, and mediation by the glucose-dependent insulinotropic peptide (GIP) in this phenomenon, as this incretin promotes insulin secretion after carbohydrate intake in insulin-sensitive, but not in insulin-resistant states.Four groups of eight metabolically healthy weight-matched postmenopausal women were provided with three isocaloric meals  containing either 30% or 60% carbohydrate, with and without two-hours of moderate-intensity exercise before the last two meals. Plasma glucose, insulin, glucagon, GIP, glucagon-like peptide 1 (GLP-1), free fatty acids (FFAs), and D-3-hydroxybutyrate concentrations were measured during 4-h postprandial periods and 3-h exercise periods, and their areas under the curve (AUCs) were analyzed by mixed-model ANOVA, and insulin resistance during fasting and meal tolerance tests within each diet was estimated using homeostasis-model assessment (HOMA-IR).The third low-carbohydrate meal, but not the high-carbohydrate meal, reduced: (1) evening insulin AUC by 39% without exercise and by 31% after exercise; (2) GIP AUC by 48% without exercise and by 45% after exercise, and (3) evening insulin resistance by 37% without exercise and by 24% after exercise. Pre-meal exercise did not alter insulin-, GIP- and HOMA-IR- lowering effects of low-carbohydrate diet, but exacerbated evening hyperglycemia.Evening postprandial insulin and GIP responses and insulin resistance declined by over 30% after three meals that limited daily carbohydrate intake to 30% compared to no such changes after three 60%-carbohydrate meals, an effect that was independent of pre-meal exercise. The parallel timing and magnitude of postprandial insulin and GIP changes suggest their dependence on a delayed intestinal adaptation to a low-carbohydrate diet. Pre-meal exercise exacerbated glucose intolerance with both diets most likely due to impairment of insulin signaling by pre-meal elevation of FFAs. Postprandial hyperglycemia >7.8 mmol/L) is a diagnostic symptom of type 2 diabetes (T2D) where it is prevalent throughout the day  and is a.8 mmol/LPostprandial hyperglycemia and hyperinsulinemia also may be affected by the particular timing of exercise with respect to high-carbohydrate meals. It is established that acute exercise increases glucose tolerance by facilitating muscle glucose uptake through a non-insulin-dependent mechanism in both metabolically healthy  and T2D The principal aim of this study was to examine the contribution of the dietary carbohydrate to the evening postprandial hyperglycemia and compensatory insulin secretion by comparing postprandial effects of carbohydrate intake at the currently recommended levels , to a lo2; with a fasting glucose level < 100 mg/dL; hematocrit > 32%, hemoglobin > 12 mg/dL; non-smokers; and without endocrine and metabolic disorders. They were volunteers recruited from the University of Michigan clinical studies webpage . All subjects signed an informed consent for human clinical studies HUM00017875 and HUM00043233 approved by The University of Michigan Medical School Institutional Review Board (IRB-MED) on 8/7/2008 and 11/11/2010, respectively. These constituent studies were registered as a clinical trial NCT01891617 with ClinicalTrials.gov after the initiation of the subject recruitment in November 2008. All subject recruitment was completed by August of 2012. The authors confirm that all trials for this intervention were registered.Subject were 32 healthy postmenopausal women, 50 to 65 years old; of body mass index (BMI) 20 to 30 kg/mSubjects underwent preliminary health and fitness screens at the Michigan Clinical Research Unit (MCRU). The health screen included health history, measurements of weight, height, and body fat by a dual-energy X-ray absorptiometry , and a fasting blood draw for fasting glucose and other laboratory chemistries. A fitness screen assessed individual maximal aerobic effort. It consisted of a treadmill test at 3 miles per hour with 2% slope increments every 3 minutes with the subject breathing through a mouthpiece using a Max II metabolic cart . The criterion of maximal effort used was a respiratory quotient of 1. After matching by body weight, BMI, and aerobic fitness, 16 subjects, each, were assigned to low-carbohydrate (LC) or high-carbohydrate (HC) meals, and, within each dietary group, 8 subjects participated either in a sedentary (LCS or HCS) or an exercise (LCX or HCX) trial.At 1800 h, the evening before the study day, subjects were admitted to MCRU. A meal containing 10 kcal/kg body weight was provided at 1900 h with the macronutrient composition appropriate to the assigned dietary group. Hourly blood collection over 24 hours was initiated at 0600 h on the study day through an antecubital vein catheter kept patent with sodium heparin. Additional samples were taken at 15- and 30-min intervals during meals and exercise. Plasma glucose, insulin, glucagon, GIP and GLP-1 responses were examined as a function of meal carbohydrate content in the presence or absence of exercise. The free fatty acids (FFAs) and D-3-hydroxybutyrate concentrations also were measured as they are elicited by reduced glucose availability after LC meals and can impair insulin sensitivity .In addition to the pre-trial evening meal, two isocaloric meals were provided at 1000 and 1700 h on the trial day , each co2 max and was completed 1 hour before each meal  and dipeptidyl peptidase-4 inhibitor . Plasma was kept frozen at -80°C for hormone and metabolite measurements. Plasma glucose  and FFAs  were measured with enzymatic colorimetric assays, and D-3-hydroxybutyrate with a kinetic enzymatic method . Plasma insulin and glucagon were measured with radioimmunoassays , and GIP and GLP-1 with a milliplex chemiluminescent assay kit . The intra-assay coefficients of variation (CV) for insulin and glucagon were respectively 2.3% and 3.6% and inter-assay CVs were 16.2% for both. For GIP and GLP-1, intra- and inter-assay CVs were <11% and <19%, respectively.Data are presented as the mean and the standard error. Hormone and metabolite areas under the curves (AUCs) were calculated by the trapezoid rule during the 4-hour early PPs (1000–1400 h and 1700–2100 h) and the 3-hour late PP (1400–1700 h) in the sedentary trial, and during exercise periods (0700–1000 h and 1400–1700 h) that included 2 hours of exercise and 1 post-exercise hour. Subject characteristics, energy consumption and expenditure, and all measured hormone and metabolites AUCs were tested for normality with Shapiro-Wilk test (W statistic), and the normally distributed data were subjected to analysis of variance  using Statistical Analysis System program . Mixed-model repeated-measures ANOVA was used to analyze the effects of diurnal meal timing and diet composition. Postprandial AUCs between LC and HC groups were analyzed as between-subject effects, while the diurnal meal timing (morning vs. evening) and the interaction between the meal timing and diet composition were analyzed as within-subject effects. For group variables that were not normally distributed, Wilcoxon rank-sum (Z statistic) test was used, and their time effects were tested with Wilcoxon signed-rank test (S statistic). Insulin resistance during the postprandial periods within each dietary condition in the presence and the absence of exercise was estimated with the homeostatic model assessment (HOMA-IR)  test forThe four experimental groups did not differ with respect to age, weight, percent body fat, BMI, or the level of aerobic fitness Tables  and 2. TPre-trial fasting plasma glucose was not different in the four experimental groups but tended to be lower after the pre-trial HC than LC meal Tables  and 2. TFasting plasma insulin at 0600 h in the morning of the sedentary trial day tended to be lower after pre-trial HC compared to LC meals  and was The HOMA-IR estimate of insulin resistance during the evening postprandial period with the LC diet was significantly lower than during the morning postprandial period in both the sedentary  and exercise  trials . There wIn the sedentary trials, postprandial glucagon AUCs after the LC meals were significantly higher in the morning  as well as in the evening  than after the HCS meals . In the Exposure to LC diet in the sedentary condition  reduced Postprandial GLP-1 responses were not affected by either diet or exercise (data not shown). However, postprandial GLP-1 AUCs were significantly higher during the afternoon, than during the morning exercise in both dietary groups .In the sedentary trial, the pre-meal rise in plasma FFAs was greater before the morning than the evening HC meal  with no such diurnal difference in LCS group . In contNo timing or dietary effects were seen on the D-3-hydroxybutyrate AUCs in the sedentary trials . In the The principal significant finding of this study was the observation of parallel declines in evening postprandial insulin and GIP responses with a concurrent decline in postprandial insulin resistance as estimated by HOMA-IR after the third exposure to the low-carbohydrate meal in metabolically healthy individuals with normal glucose tolerance. This outcome was unaffected by pre-meal exercise and absent after the high-carbohydrate meals. The remarkable features of this finding were that postprandial insulin and GIP declines to the low-carbohydrate, relative to the high-carbohydrate, meals were strikingly similar both in their magnitude under both the sedentary and the exercise conditions  and that they also were synchronous in the timing of their decline. A significant decline in the postprandial GIP AUC after the morning meal preceded the simultaneous declines in both the postprandial GIP and insulin responses after the third low-carbohydrate meal in the evening suggesting a probable causative role of the GIP incretin decline in the evening postprandial insulin decline. This observation prompts two tentative conclusions.The first one is that the decline in the evening postprandial insulin response and in the estimate of insulin resistance after a 24-hour exposure to low-carbohydrate meals is driven by the intestinal processing of carbohydrates rather than by any changes in muscle glucose uptake as the latter would have been affected by pre-meal exercise. The likely sequence of effects produced by a third exposure to a low-carbohydrate meal is sensing of reduced glucose concentration in the proximal brush-border epithelium by the small intestinal glucose sensor, the sodium-glucose co-transporter-1 (SGLT1) ,45. In cOur second tentative conclusion is that a 24-hour delay associated with three exposures to low-carbohydrate diet in the lowering of postprandial insulin response, most likely reflects the latency in the intestinal adjustment to the changes in the carbohydrate load. Although we did not investigate the type of diets habitually eaten by the 32 subjects in our study, it is highly probable that it approximated the 51% dietary carbohydrate content reported for the US population in 2011 . Both phThe significant reduction in the evening postprandial insulin response and HOMA-IR estimate of insulin resistance after a day’s exposure to low-carbohydrate meals, raises two questions about the contribution of daily carbohydrate load to this effect. The first one addresses the absolute size of carbohydrate load responsible for the changes in the evening postprandial insulin response, and insulin resistance. Reducing acute dietary carbohydrate exposure in three steps by a factor of two, from 125 to 31.25 g of carbohydrate per meal using a 50% carbohydrate diet, abolished evening hyperglycemia with 31.25 g of carbohydrate per meal or a with total daily carbohydrate load of 95 g . At 62.5The second question addresses the influence of the timing of exposure to high carbohydrate load on the evening postprandial glycemic and insulin responses. This is relevant to our study since we offered two relatively large iso-caloric 740 to 820 Kcal meals rather than the customary three smaller daily meals. Two and a half-fold higher evening compared to morning postprandial glycemia after the high-carbohydrate relative to low-carbohydrate meals (12.3% vs 4.9%) may have been influenced by delivery of 120 g as opposed to 58 g of carbohydrate in the evening meal. Greater carbohydrate loads in the evening than in the morning were shown to produce evening glucose intolerance is several studies. When 60% of daily calories and 181 g of carbohydrate in a 54.5% carbohydrate diet were eaten at dinnertime, evening postprandial glycemia was 20% greater than when this large meal was eaten at breakfast . AdditioPresence of high evening postprandial insulin response, and HOMA-IR score in our study during acute one-day exposure to 60% carbohydrate diet in metabolically healthy, glucose tolerant individuals raises the question of whether this effect may become clinically significant with extended exposure to this diet in view of the health risks of chronic hyperglycemia and hyperinsulinemia of Type 2 diabetes  and two 2 max [2 max [Pre-meal exercise exacerbated evening glucose intolerance under both dietary conditions. At 45% of maximal effort, lipid utilization contributed between 57 and 59% of energy for pre-meal exercise in both of our dietary groups as was also documented by others . We obse2 max –62 but nx [2 max . Each twx [2 max ,64.For an explanation of high HOMA-IR scores during the morning postprandial periods after both LC and HC diets, and during the second postprandial period after HC meals, it is useful to view the fasted or post-absorptive states as conditions of muscle fuel repletion and of shortage of circulating fuel. At the start of the morning postprandial period, fasting muscle is glycogen and lipid replete  and assoThis study has several limitations. We did not directly measure peripheral glucose uptake or insulin sensitivity by the intravenous glucose tolerance test or by the hyperinsulinemic euglycemic clamp. Despite their established advantages in assessing insulin sensitivity, these methods bypass gastrointestinal route of carbohydrate intake and require testing of the effects of experimental manipulations with identical glucose and insulin challenges. This made them unsuitable for the assessment of changes in postprandial glycemia to oral intake of meals differing in carbohydrate content. We chose the HOMA-IR estimate of insulin resistance during the meal tolerance tests in part because it allows assessment of the physiological contribution of intestinal incretins to insulin secretion. This is a surrogate test for the intravenous frequently-sampled glucose tolerance test against which it has been validated , 43. TheDespite these limitations, our results and consideration of the work of others allow us to make the following conclusions: First, a third successive meal containing about 57 g of carbohydrates and contributing to a cumulative daily carbohydrate load of about 115 g, significantly reduces evening postprandial insulin and GIP responses and HOMA-IR estimate of insulin resistance, while the same number of iso-caloric meals containing about 120 g of carbohydrates and a daily carbohydrate load of 241 g contributes to evening postprandial hyperinsulinemia and a high HOMA-IR score. Second, the parallel three-meal or 24-hour delay in the onset, and a similarity in the magnitude, of declines in postprandial insulin and GIP responses suggests their probable interdependent responses to a low-carbohydrate diet. The three-meal delay in the reduction of the evening postprandial insulin and GIP response appears to represent a delay in the adaptation of intestinal glucose sensing to the intake of low-carbohydrate meals. Third, the decline in the evening postprandial insulin, GIP, and HOMA-IR to third exposure to a low-carbohydrate meal is a dietary effect unaffected by any changes in muscle glucose uptake during pre-meal exercise. In effect, pre-meal moderate-intensity exercise exacerbates evening glucose intolerance regardless of the dietary composition of the meals and without an effect on the dietary influence over evening postprandial insulin, GIP, and HOMA-IR. The exercise effect is most likely mediated through its stimulation of endogenous glucose production ,27 compoS1 Checklist(PDF)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.