The present study examined the effects of excitotoxic lesions in 2 closely related structures, the anterior thalamic nuclei and the retrosplenial cortex, on latent inhibition. Latent inhibition occurs when nonreinforced preexposure to a stimulus retards the subsequent acquisition of conditioned responding to that stimulus. Latent inhibition was assessed in a within-subject procedure with auditory stimuli and food reinforcement. As expected, sham-operated animals were slower to acquire conditioned responding to a stimulus that had previously been experienced without consequence, relative to a non-preexposed stimulus. This latent inhibition effect was absent in rats with excitotoxic lesions in the anterior thalamic nuclei, as these animals conditioned to both stimuli at equivalent rates. The retrosplenial lesions appeared to spare latent inhibition, as these animals displayed a robust stimulus preexposure effect. The demonstration here that anterior thalamic nuclei lesions abolish latent inhibition is consistent with emerging evidence of the importance of these thalamic nuclei for attentional control. In line with their dense interconnections with the hippocampus, the rodent anterior thalamic nuclei (ATN) are vital for spatial memory and navigation . The ATNLike the ATN, the retrosplenial cortex (RSC) is heavily implicated in spatial learning and memory , but theTo examine further the involvement of these closely interconnected structures in stimulus processing, the current set of experiments assessed the impact of lesions in the ATN and RSC on latent inhibition. Latent inhibition is demonstrated when nonreinforced preexposure to a stimulus retards subsequent learning about that stimulus . This laConsequently, this study assessed the impact of excitotoxic lesions within the ATN (Experiment 1) and RSC (Experiment 2) on latent inhibition. Latent inhibition was assessed in a within-subject conditioning paradigm using auditory stimuli and food reinforcement. Preexposing one of the auditory stimuli results in retarded acquisition of conditioned responding to that stimulus relative to another, non-preexposed auditory stimulus. The key question was, therefore, whether excitotoxic lesions in either the ATN or RSC would disrupt the usual retardation effect.n = 16) or surgical shams . All procedures were carried out in accordance with the U.K. Animals (Scientific Procedures) Act (1986) and EU directive (2010/63/EU), as well as being approved by local ethical committees at Cardiff University. The experiment began approximately a month after surgery. Prior to the current experiment, the rats were tested on spatial alternation in a T maze.Experiment 1 involved a cohort of 28 male Lister Hooded rats  weighing between 250 and 295 g at the time of surgery. Animals were housed in pairs under diurnal light conditions (14 hr light/10 hr dark). Behavioral testing occurred during the light phase. The rats were handled daily for a week prior to surgery and then randomly assigned to one of two surgical groups: ATN lesions  and 0.063 M ibotenic acid  solution diluted in phosphate-buffered saline  into two bilateral sites via a 26-gauge 1-μl Hamilton syringe . The anterior-posterior (AP) and dorsal-ventral (DV) coordinates were measured (in millimeters) from bregma and the mediolateral (ML) coordinates (in millimeters) from the sagittal sinus. The stereotaxic coordinates were as follows: (1) −0.1/0.2 (AP), ± 0.8 (ML), −6.8/6.9 (DV) and (2) −0.2/0.3 (AP), ± 1.5 (ML), −6.2/6.3 (DV). A volume of 0.16 μl of the NMDA/ibotenic acid cocktail was injected at Site 1 and a volume of 0.2 μl at Site 2. Injections were made at a rate of 0.05 μl a minute. After each injection, the needle was left in situ for 5 min. Rats in the surgical control (Sham1) group received identical treatment, except that no neurotoxin was infused. On completion of the surgery, the skin was sutured and clindamycin antibiotic powder  was applied topically, and animals received a subcutaneous injection of glucose saline (5 ml). All animals were given a minimum of 10 days of postoperative recovery before the commencement of T maze behavioral testing.Anesthesia was induced in all animals using a mixture of oxygen and isoflurane gas before placing them in a stereotaxic frame , with the incisor bar set at + 5.0 mm to the horizontal plane. Analgesics, Metacam  and lidocaine , were administered subcutaneously. A midline sagittal incision was made in the scalp, and the skin was retracted to expose the skull. A craniotomy was made above the injection sites. The lesions were made by injecting 0.068 M All training was conducted in a set of eight operant boxes , each measuring 240 mm high × 240 mm deep × 300 mm wide. The boxes were arranged in two rows of four. Each box had two aluminum walls, with a clear Perspex front, back, and ceiling. The grid floor comprised 19 parallel stainless steel bars spaced 16 mm apart. Each operant box was housed in its own sound and light attenuating chamber. Each chamber was illuminated by a 3-W house light located at the top center of the left wall. Auditory stimuli consisted of a 2-kHZ tone and 10-Hz train of clicks, both delivered through ceiling speakers. During training, sucrose pellets  were delivered into a recessed food magazine situated in the center of the right-hand wall of the operant box. The magazines were fitted with a pair of infrared detectors that recorded magazine entries. Equipment control and data recording were via a PC equipped with MED-PC software .Latent inhibition was examined in a within-subject appetitive conditioning procedure as described by In each session, the house light was illuminated at the start of every session and remained on for the entire session.All rats received two magazine training sessions, during which a single-pellet reinforcer was delivered into the magazine five times in each 30-min session, according to a random interval 6-min schedule.All rats received three 40-min preexposure sessions, during which one of two auditory stimuli, either a 3-kHz tone or a 10-Hz clicker , was presented 12 times per session with a mean interstimulus interval of 3 min . Each stimulus presentation lasted 30 s. No reinforcement was given during these sessions.All rats were then conditioned to both auditory stimuli over six 40-min sessions. In each session, there were two presentations of each of the two auditory stimuli; that is, the preexposed conditioned stimulus (CS) and the non-preexposed stimuli were each presented twice. Stimuli were presented on a random interval 7-min schedule, and the order of presentation was counterbalanced. Stimulus presentation lasted 30 s, and a single reinforcer was delivered into the magazine at the end of each stimulus presentation. Magazine activity was measured during a 30-s period prior to the onset of the CS (pre-CS) as well as during the 30-s CS presentation.On completion of behavioral testing, rats were deeply anesthetized with sodium pentobarbital  and transcardially perfused with 0.1 M PBS followed by 4% paraformaldehyde in 0.1 M PBS (PFA). The brains were removed and postfixed in PFA for 4 hr before being transferred to 25% sucrose, in which they remained for at least 12 hr at room temperature, with gentle agitation. A one-in-four series of coronal sections (40 μm) was cut on a freezing microtome and collected on gelatin-coated slides, then subsequently stained using the Nissl stain, cresyl violet.Magazine entry behavior was assessed by examining the total number of entries during the stimulus presentation minus the total number of magazine entries during the 30-s pre-CS period. Data were analyzed in a three-way analysis of variance (ANOVA) with a between-subjects factor of group (ATN vs. Sham1) and within-subjects factors of stimulus (preexposed vs. non-preexposed) and session (1–6). To control for between-subjects variability and to construct error bars appropriate for within-subject comparisons  and 3, tThe three ATN cases with the smallest anterior thalamic lesions also had evident fornix disruption. These three cases were accordingly excluded. In addition, two shams with unexpected damage to the fornix were also excluded, leaving groups sizes of ATN = 13 and sham controls = 12 .Among the 13 ATN cases, there was consistent, bilateral cell loss within the ATN. The lesions were centered in the anteromedial and anteroventral thalamic nuclei, although the anteroventral cell loss was asymmetric in three cases, and all cases involved cell loss in the anterodorsal nucleus. Those animals with essentially complete anterior thalamic lesions also had cell loss in the parataenial nucleus (three cases) and the paraventricular nucleus (four cases). Seven cases also had discrete cell loss in restricted parts of the nucleus reuniens.F = 12.2, p < .01, but was unaffected by lesion group .Baseline magazine entry activity increased from across the two sessions, F < 1). There was an effect of session, F = 4.3, p < .001, but no effect of CS (F < 1) or any interactions between session and lesion . Data from the conditioning sessions are, therefore, presented as magazine activity during CS presentation minus magazine activity during the pre-CS period.Magazine entry behavior in the 30 s prior to the onset of the CS (pre-CS epoch) did not differ by lesion group  = 20.54, p < .001; no effect of CS, F = 2.32, p = .14, or lesion, F = 1.01, p = .33; and no interactions between session and lesion (F < 1), or session and CS, F = 1.69, p = .14, or a three-way interaction between these factors (F < 1). However, ANOVA did reveal an interaction between lesion and CS, F = 4.99, p < .05. Subsequent simple effects confirmed a clear latent inhibition effect in the Sham1 group, F = 10.97, p < .01, but not in the ATN group (F < 1). Furthermore, conditioning to the non-preexposed stimulus differed by lesion group, F = 6.47, p < .01, but there were no differences between the two groups in the level of conditioning to the preexposed stimulus.n = 16) or surgical shams . All procedures were carried out in accordance with the U.K. Animals (Scientific Procedures) Act (1986) and EU directive (2010/63/EU), as well as being approved by local ethical committees at Cardiff University. Behavioral training took place approximately 6 months after surgery. Prior to the current experiment, the animals had been tested on a strategy shift task and matching to place in a T maze  weighing between 309 and 356 g at the time of surgery. Details of housing and husbandry are the same as described for Experiment 1. Rats were randomly assigned to one of two groups prior to surgery: RSC  with the nose-bar set at + 5.0. Lidocaine  was administered by subcutaneous injection to the scalp before it was incised and retracted. A bilateral craniotomy, extending from bregma to lambda, exposed the cortex along the midline. Lesions were produced by injecting a solution of 0.09 M NMDA , dissolved in phosphate buffer (pH 7.2), via a 1-μl Hamilton syringe , at six bilateral sites within the RSC, at a rate of 0.05 μl per minute. After each injection, the needle was left in situ for 5 min.Before the induction of anesthesia, animals received an intraperitoneal injection of atropine  and subcutaneous Metacam . Rats were then deeply anesthetized  with 6% sodium pentobarbital solution , and anesthesia was maintained with isoflurane (∼0.5%) in OAP coordinates were measured (in millimeters) from bregma, the ML coordinates (in millimeters) from the sagittal sinus, and the DV coordinates (in millimeters) with reference to the level of the dura. The stereotaxic coordinates at each of the six sites were as follows: (1) −1.8 (AP), ± 0.5 (ML), −1.0 (DV); (2) −2.8 (AP), ± 0.5 (ML), −1.1 (DV); (3) −4.0 (AP), ± 0.5 (ML), −1 (DV); (4) −5.3 (AP), ± 0.5 (ML), −2.5 (DV); (5) −5.3 (AP), ± 0.9 (ML), −1.4 (DV); and (6) −6.6 (AP), ± 0.9 (ML), −1.8 (DV). A volume of 0.25 μl NMDA was injected at Sites 1–3 and 0.27 μl at Sites 4–6.On completion of the surgery, the skin was sutured and clindamycin antibiotic powder  was applied topically, and animals were rehydrated with glucose saline . Surgical shams received the identical procedure except that the needle was not lowered into the cortex and no NDMA injections were made. All rats received a minimum of 10 days postoperative recovery.The same operant chambers were used as for Experiment 1 except that chocolate flavor food pellets served as reinforcement .All behavioral training was conducted as described for Experiment 1.n = 16) and Sham2 (n = 12).All of the 16 RSC rats displayed considerable cell loss in both the granular and dysgranular subregions. In one rat, there was, however, bilateral sparing of granular RSC rostral to the splenium. A further six animals had minimal sparing of granular A cortex caudal to the splenium. One of these six also had unilateral sparing in the most rostral part of the RSC. Of the 16 RSC animals, 9 had a limited degree of bilateral hippocampal damage . The extFs < 1; mean ± SEM magazine entries: RSC = 250.7 ± 16.95; Sham2 = 266.5 ± 12.4).Magazine approach behavior during the two training sessions did not differ by lesion group, and there was no effect of session or a Session × Group interaction . There was an effect of session, F = 4.4, p < .001, but no effect of CS (F < 1) or any interactions .Magazine approach activity in the 30 s prior to the onset of the CS (pre-CS epoch) did not differ by lesion group  = 9.24, p < .005, and session, F = 17.35, p < .001; a Session × CS interaction, F = 2.99, p < .05; but no effect of lesion (F < 1) or interaction between CS and lesion (F < 1). However, ANOVA also revealed a three-way interaction between session, CS, and lesion, F = 3.78, p < .01. Subsequent tests revealed a simple interaction effect between lesion and CS in Session 2, F = 10.39, p < .01, as the Sham2 group showed a clear latent inhibition effect in Session 2, F = 17.04, p < .01, but this effect appeared absent in the RSC group (F < 1). There was also a simple interaction effect between lesion and CS in Session 3, but here the RSC group showed a clear latent inhibition effect, F = 14.89, p < .01, whereas there was no difference in responding to the two stimuli in the Sham2 group (F < 1).As is clear from The current set of experiments examined the impact of damage within the ATN (Experiment 1) and RSC (Experiment 2) on latent inhibition using a within-subject appetitive procedure. As expected, sham-operated rats were slower to acquire conditioned responding to a stimulus that had previously been experienced without consequence relative to a non-preexposed stimulus. This stimulus preexposure effect was absent in the ATN group, as these animals conditioned to the preexposed and non-preexposed stimuli at equivalent rates. In contrast, RSC lesions did not disrupt latent inhibition.On initial inspection, it might be supposed that the ATN lesions did not disrupt latent inhibition per se but had a more general effect on the acquisition of appetitive Pavlovian conditioning, leading to a nonselective effect on responding irrespective of stimulus preexposure. Although there was no overall effect of lesion, responding to the non-preexposed stimulus was initially lower in the ATN group relative to control animals, but it did reach comparable levels by the end of training. In off-baseline between-subjects assessments of latent inhibition, interpreting a lack of difference in responding between non-preexposed and preexposed animals is problematic when this effect may be mediated by changes in the level of conditioning in the non-preexposed group . HoweverThe theoretical basis of latent inhibition effect remains moot, as a number of different hypotheses could be put forward to explain ATN lesion-induced disruption of the stimulus preexposure effect. Several prominent theories of latent inhibition posit that the phenomenon arises because associations established between the CS and context during the preexposure phase interfere with either the acquisition or expression of CS → reward associations formed during conditioning . Thus, iOther theoretical accounts attribute latent inhibition to the conditioning of inattention or to decrements in the associability of, or attention to, the preexposed stimulus . AccordiIt is striking that the abolition of latent inhibition following lesions in the ATN contrasts with the behavioral effects of damage in related structures, most notably the prefrontal cortex and hippocampus, both sites that are heavily interconnected with the ATN . PrefronT maze (Experiment 2 revealed that RSC lesions were without any apparent effect on latent inhibition. Although the expression of latent inhibition appeared to be delayed in these animals relative to their surgical controls, this may reflect variability in the surgical sham group rather than an effect driven by the RSC lesions. In any event, once the latent inhibition effect emerged, the RSC lesion animals exhibited a robust stimulus preexposure effect that was numerically, if not statistically, more enduring than in the controls animals. This null result is perhaps surprising given previous evidence of attenuated neuronal responses to a preexposed stimulus in the RSC . The preT maze . Rather,T maze .A further possibility is that the experimental parameters used in the current study were not sufficiently sensitive to detect retrosplenial involvement in latent inhibition. For example, hippocampal lesion effects on latent inhibition are only apparent when there is a shift in context between preexposure and test , while fA further notable feature of the current findings is that ATN and RSC lesions produced dissociable effects on latent inhibition. Thus, despite being heavily interconnected , 2003, tIn summary, pretraining rats with excitotoxic lesions in the ATN, but not the RSC, disrupted the usual retardation in conditioning to a stimulus that has previously been experienced without consequence. These findings add to an emerging literature implicating the rodent ATN in cognitive functions beyond the spatial domain and, in particular, in attentional control . The goa


Deficient inhibitory control in Parkinson’s disease (PD) is often observed in situations requiring inhibition of impulsive or prepotent behaviors. Although activation of the right-hemisphere frontal-basal ganglia response inhibition network is partly altered in PD, disturbances in interactions of these regions are poorly understood, especially in patients without cognitive impairment. The present study investigated context-dependent connectivity of response inhibition regions in PD patients with normal cognition and control participants who underwent fMRI while performing a stop signal task. PD participants were tested off antiparkinsonian medication. To determine if functional disturbances depended on underlying brain structure, aberrant connectivity was correlated with brain volume and white-matter tissue diffusivity. We found no group differences in response inhibition proficiency. Yet the PD group showed functional reorganization in the long-range connectivity of inhibition regions, despite preserved within network connectivity. Successful inhibition in PD differed from the controls by strengthened connectivity of cortical regions, namely the right dorsolateral prefrontal cortex, pre-supplementary motor area and right caudal inferior frontal gyrus, largely with ventral and dorsal attention regions, but also the substantia nigra and default mode network regions. Successful inhibition in controls was distinguished by strengthened connectivity of the right rostral inferior frontal gyrus and subcortical inhibition nodes . In both groups, the strength of context-dependent connectivity correlated with various indices of response inhibition performance. Mechanisms that may underlie aberrantly stronger context-specific connectivity include reduced coherence within reorganized systems, compensatory mechanisms, and/or the reorganization of intrinsic networks. In PD, but not controls, abnormally strengthened connectivity was linked to individual differences in underlying brain volumes and tissue diffusivity, despite no group differences in structural variables. The pattern of structural-functional associations suggested that subtle decreases in tissue diffusivity of underlying tracts and posterior cortical volumes may undermine the enhancement of normal cortical-striatal connectivity or cause strengthening in cortical-cortical connectivity. These novel findings demonstrate that functionally reorganized interactions of inhibition regions predates the development of inhibition deficits and clinically significant cognitive impairment in PD. We speculate that altered interactions of inhibition regions with attention-related networks and the dopaminergic system may presage future decline in inhibitory control. Executive dysfunction is the most frequently reported cognitive disability in Parkinson’s disease (PD). A feature of executive dysfunction is diminished response inhibition, which affects the selection of behaviors and decisions about when or whether to act. Response inhibition deficits in PD are found on a variety of tasks  requiring the inhibition of impulsive or prepotent responses , and arede novo PD patients  . The StNpatients , suggestNotably, much of what we know about changes in the response inhibition network in PD comes from analyses of regional activation, which are insensitive to disturbances in the interactions of inhibition nodes amongst themselves or with other brain regions. Recently, functional connectivity between the right IFG and the striatum during successful inhibition  was reported to be weaker in PD ON patients than in controls , but conThe present study builds upon previous research by characterizing disease-related disturbances in the context-dependent connectivity of regions associated with response inhibition processes. Response inhibition is thought to involve a right-hemisphere biased frontal-striatal-subthalamic network . HoweverThe Institutional Review Board at the VA San Diego Healthcare System approved the study. All subjects provided signed written informed consent. The sample consisted of 28 PD participants who met the PD United Kingdom Brain Bank Criteria and 29 healthy controls. Exclusion criteria included metal in the head, neurological diagnoses other than PD, psychiatric diagnoses, history of alcohol or substance abuse, positive MRI findings , use of anticholinergics or cognitive medications , and complaints of cognitive deficits. PD volunteers with axial tremors or upper/lower limb tremors that might cause head motion were excluded. Volunteers were excluded if they met the Movement Disorders Society Level II criteria for PD-MCI , hereaftn = 2), levodopa monotherapy (n = 5), or levodopa combination therapy (n = 21), and were in Hoehn and Yahr stages 1 (11%), 2 (53%) and 3 (36%). UPDRS total motor, tremor, and postural instability/gait disorder (PIGD) symptoms , but the control group had a greater percentage of females Table . PD partsymptoms  were sigapy n = 2, and werThe SST consisted of a series of Go (75%) and Stop (25%) reaction-time trials that were presented in a pseudorandom order  . Each trImaging was conducted on a GE MR750 Discovery 3 Tesla system with an eight-channel head coil. Head motion was limited by foam pads inserted between the head and the coil. Visual stimuli were viewed through a NordicNeuroLab goggle system. Non-ferrous key pad devices interfaced with a computer recorded task performance during fMRI for off-line analysis.b-value = 1000 s/mm2, six non-diffusion weighted images (bo), slice thickness 2.0 mm, TR = 9.2 s, TE = minimum, matrix = 128 × 128mm, FOV = 25.6 mms, and voxel size = 2.0 × 2.0 × 2.0 mm), and fat suppression.High-resolution T1-weighted anatomical images maximized differentiation of the white and gray matter boundary . For fMRI, echo-planar images (EPI) were acquired in an oblique orientation (perpendicular to the anterior-posterior commissure) to minimize susceptibility artifacts, using a single-shot, blipped, gradient-echo, EPI pulse sequence  that provided coverage of the entire brain. Whole-brain axial diffusion tensor images (dMRI) were acquired on 3.0T GE MR750 using a single-shot EPI sequence with diffusion-encoding along 51 directions, F = 2.7, p > 0.10, SD = 0.46; PD mean = 0.69 mm, SD = 0.39), which was <2 mm in all subjects. Likewise, no group differences were found in framewise displacement . Thus, procedures for restricting head motion were highly effective. Motion correction (SLOMOC) further reduced small fluctuations in head motion . After motion correction, the volumes were time shifted, transformed to Talairach space, and spatially filtered (6 mm Gaussian kernel).Data were processed and analyzed using the Analysis of Functional NeuroImages (AFNI) software . After dFirst-level voxel-wise analyses tested for the effect of the task conditions on brain activation for each group to verify that during SST performance commonly reported regional patterns of brain activation were produced. We did not expect to find condition effects for the StN and SN, since whole-brain voxel-wise analyses are often insensitive to activation differences between conditions in small volumes. As such, the StN and SN were also included in our context-dependent connectivity analyses (see below). AFNI 3dDeconvolve was used to estimate the hemodynamic response function (HRF) of each voxel using multiple linear regressions. The analysis pipeline included deconvolution of each subject’s time series for each experimental condition  to generate an HRF of the signal on a voxelwise basis. Each HRF was estimated relative to the baseline state. Incorrect Go trials were regressed out of the time series at each voxel. The main dependent measures were the differences in magnitude of the signal (beta coefficient) for the SI > Go and the SI > FI conditions. The latter comparison controls for the greater salience of stop trials relative to Go trials and probes for activity related to stopping accuracy.p < 0.05 was obtained using a voxelwise probability of p < 0.002 and a minimum cluster size of ≥14.7 voxels for the cortex and ≥4.7 voxels for the basal ganglia.The effects of task condition on brain activation in each group  were tested using 3dMVM . Monte Cp < 0.05 was obtained for tests of group differences using a voxelwise probability of p < 0.005 and minimum clusters sizes of 8 voxels (SI > Go) and 6.3 voxels (SI > FI).Based on the results from the first-level analyses, ROI maps were generated by combining the regions that showed a condition effect of interest for each group. Then second-level voxelwise ANCOVAs (sex adjusted) tested for group differences in regions that showed the above condition effects (3dMVM). Based on 5,000 Monte Carlo simulations (3dClustSim using the ACF method) of each condition map, a corrected alpha of z transforms were applied to the correlation maps to test for group differences in context-dependent connectivity. Group comparisons (adjusted for sex) were thresholded using a voxelwise-probability of p < 0.005 and minimum cluster size of 24 voxels for cortical connectivity and 8 voxels for subcortical connectivity . The false discovery rate (FDR) method was applied to the corrected p values to further adjust for analyses of multiple seeds.The main focus of the hypotheses was to test whether context-dependent connectivity of a seed ROI with other brain regions  differed between the groups. The generalized psychophysical interaction (gPPI) method, as implemented in AFNI software, was used since beta estimates are more sensitive and specific to context-dependent connectivity than the standard PPI method . 5 mm diProcessing and analysis of dMRI data was conducted using Functional Magnetic Resonance Imaging of the Brain (FMRIB) software library 5.0.8 (FSL). After motion and eddy-current correction, data for each subject were fit on a voxel-by-voxel basis to the diffusion tensor model, accounting for floor bias with a maximum likelihood estimation approach. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) were calculated from the diffusion tensor in each voxel. Data were then processed using Tract-Based Spatial Statistics preprocessing functions . FA imag1. Briefly, each subject’s MRI volume was linearly registered to Talairach space, bias-field corrected and then a high-dimensional, non-linear registration to Talairach space was performed. Each voxel of the volume was automatically assigned a label based on probabilistic estimations relying on Markov random fields , except where noted. Post-processing of dMRI data regressed out sex effects, rendering it unnecessary to compute standardized residuals for these measures.Owing to group differences in sex, analyses were conducted on sex-adjusted standardized residuals computed for behavioral  and MRI (rsfMRI and brain volume) variables. The FDR method adjusted for multiple analyses  and clinical variables (FDR adjusted).To determine if activation was associated with SST measures, regions comprising the right-hemisphere inhibition network that showed greater SI than Go activation were extracted for each subject. Functional and SST measures  were converted to sex adjusted standardized residuals and then correlated, separately for each group. Pearson correlation analyses also tested for associations between SST measures and the standardized residuals of aberrant PPI measures that showed greater SI than Go connectivity. Owing to the Lastly, discriminant function analyses with classification were performed on MRI variables that significantly differed between the groups to identify sensitive signatures of neuropathological changes in PD, which could inform the development and refinement of measures that have potential to serve as markers in longitudinal studies. To reliably estimate classification accuracy, a bias-corrected accelerated bootstrap  method was used . ReceiveAnalyses of covariance  showed that the PD group had significantly lower scores than controls on the Letter Fluency test, but no other group differences were found on the remaining neuropsychological tests Table . While tF = 90.1, p = 2.99E-10; PD: F = 112.8, p = 3.84E-11), consistent with the independence assumption of the race model of stopping . Moreover, all participants showed longer Go RTs than failed mean RTs. There were no significant group differences in the main measures, namely SSRT and SSD. SSRT did not correlate with Go RT in either group , indicating that slowing did not significantly affect SSRT estimates. Altogether, these results are consistent with the independence assumption of the race model, which generally provides a good account of stop-signal performance in a variety of tasks and different populations (ANCOVA (adjusting for sex) was used to test for group differences in the SST measures Table . Stop trstopping . This diulations .p < 0.002) were typically similar for the PD and control groups . No other correlations were significant. In the PD group, greater SI activation in the preSMA and the right BA 44 were associated with lower SSD values  and prolonged SSRT (worse) . In addition, greater SI than Go activation of the right anterior insula was associated with lower SSD values . Response inhibition performance was not associated with SI > FI activation in either group.To determine if brain activity during successful inhibitions relative to Go trials was associated with inhibition proficiency, individual variations in activation of the inhibition network  were correlated with SSD and SSRT, separately for each group. In the control group, greater SI than Go activation in right BA 44 was associated with lower SSD values (worse inhibitory control) (p < 0.0001) and FI trials (p < 0.0006). No other significant group effects were found.Relative to controls, the PD group showed greater activation in the right lingual gyrus for SI trials in comparison to Go ; no such association was found in the PD group (p > 0.29). In the PD group, stronger rIFG – left fusiform/culmen connectivity was associated with higher SSDs (better) ; no such relationship was found in the control group (p > 0.26). As for SI > FI connectivity, stronger right SN and StN connectivity with left precuneus/IPL for SI trials was associated with slower SSRTs in the control group . No significant relationships between inhibition proficiency measures and SI versus FI connectivity were found in the PD group (p > 0.15).Next, we examined relationships (sex adjusted) between context-dependent connectivity and the speed or proficiency of inhibition, as measured by SSRT and SSD. In the control group, stronger right caudate – left IPL connectivity for SI trials (relative to Go) was associated with slower SSRTs (worse)  were not found for any white-matter tracts. We then examined if individual variations in tissue diffusivity within each group correlated with measures of aberrant context-dependent connectivity. We focused on aberrant PPI variables associated with greater SI than Go connectivity Table .Correlations were constrained to analyses of FA in tracts underlying the group differences in corticostriatal and cortico-cortical context-dependent connectivity for successful inhibitions relative to Go trials. The analyses included the following tracts: (1) the corticospinal tract (CST), which is concerned with motor function; (2) the anterior and posterior limb of the internal capsule  and external capsule (EC), projection tracts containing connections from the thalamus/basal ganglia to the cerebral cortex; (3) the superior longitudinal fasciculus (SLF), a major association tract that supports cortico-cortical communication; and (4) the body of corpus callosum (CC), a commissural tract that supports interhemispheric interactions. Pearson correlations were conducted separately for each group and FDR adjusted for multiple analyses over six tracts for each PPI variable.r = 0.53, p < 0.004), ALIC , PLIC ), EC  and CC  . Significant associations were not found between the other PPI variables and FA for either group. In the PD group, FA in tracts did not correlate with SSD or SSRT. In controls, higher FA in the ALIC  and the CC  was associated with faster response inhibition (SSRT).In the PD group, stronger right caudate connectivity with the left inferior parietal cortex was associated greater FA in the CST , temporal , and occipital  volume, but not frontal lobe or basal ganglia volume . Associations were not found between the other PPI variables and regional volumes for either group (FDR adjusted). In both groups, larger temporal  and parietal  lobe volumes were associated with faster response inhibition (SSRT).In the PD group, stronger connectivity of the right DLPFC with left middle/inferior temporal cortex during successful inhibitions  was associated with reduced parietal , attention , and verbal fluency  (FDR adjusted). No relationships were found with SSD. Disease duration and levodopa dosage equivalence did not significantly correlate with SST measures, nor did motor symptoms  ON or OFF medication. PPI measures did not correlate with neuropsychological or clinical variables.In the PD, but not control group, slower SSRTs correlated with poorer executive functioning, including worse inhibition and cognitive flexibility , whereas successful inhibition in controls was distinguished by strengthened context-dependent connectivity of the rIFG and basal ganglia . In controls, stronger connectivity of the basal ganglia, but not the rIFG, was related to inhibition proficiency and/or accuracy (accurate versus failed stopping). By comparison, in PD aberrantly stronger DLPFC and preSMA connectivity was associated with inhibition accuracy, but not proficiency. However, patients who exhibited more proficient inhibitory control also showed a pattern of stronger rIFG connectivity that was found in the control group. The absence of a relationship between stronger connectivity and inhibition proficiency, but not accuracy, may relate to reduced coherence in functionally reorganized systems, compensatory mechanisms, and/or the reorganization of intrinsic networks. In PD participants only, poorer executive functioning also correlated with worse response inhibition, but not aberrant context-dependent connectivity. This too indicates that contextually dependent functional reorganization does not necessarily correlate with all facets of cognitive control. Connectivity measures were highly sensitive in distinguishing PD from control participants, unlike the magnitude of fMRI activation within the inhibition network (voxel-based fMRI) and tissue diffusivity (FA), which did not differ between the groups, and frontal lobe volume, which showed poor classification accuracy. Lastly, the strength of some effective connectivity measures during successful inhibition was partly related to structural variations in the brain of PD participants, but not controls, despite an absence of group differences in these measures. This result may suggest that altered connectivity of the inhibition network presages future changes in underlying brain structure.Recently, dynamic causal modeling (DCM) among four inhibition regions  failed to characterize the interactions amongst frontal and subcortical inhibition areas in PD ON participants , as it dTo the best of our knowledge this is the first study of PD to demonstrate a context-dependent functional reorganization in the long-range connectivity of regions that are frequently implicated in response inhibition processes. Aberrantly strengthened connectivity was largely with temporal-occipital and parietal cortices, which are elements of the ‘bottom–up’ ventral and ‘top–down’ dorsal attention networks, respectively . SpecifiInterestingly, inhibition proficiency in PD was not correlated with abnormally stronger connectivity of cortical inhibitory control centers. One explanation is that some context-specific changes  are fundamentally due to intrinsic network reorganization in PD, which can influence cognitive control . To evalAs for healthy adults, dorsal and ventral attention networks are activated by different inhibition processes , although to different degrees , indicatWith respect to the above findings, it is noteworthy that interventional treatments for deficient inhibitory control that target executive processes have only a modest effect on behavior . Yet intWe also found that strengthened caudate, SN, and StN connectivity with the dorsal attention and DMN was stronger for inhibition successes than failures in controls, but not in PD patients. The StN is the subcortical node of the fast hyperdirect pathway that receives direct input from the prefrontal cortex, allowing cortical input to directly influence the StN , which iSurprisingly, group differences were not found in the connectivity strength of the right anterior insula, a key region of the salience and inhibition networks. Although the magnitude of right anterior insula activation also did not differ between the groups, greater insula activation during successful inhibitions  was associated with worse inhibition proficiency (lower SSD) in PD participants, but not controls. We speculate that this disease-specific neurobehavioral association in cognitively normal PD may presage the future development of response inhibition deficits. This observation is important as the level of striatal dopamine depletion in PD-MCI is linked to D2 receptor availability in the insula, which in turn correlates strongly with poorer executive functioning . LongituWe reported earlier  that tisAs for group differences in brain volume, only frontal lobe volume was significantly reduced in the PD group, contrary to other studies of PD without MCI . DespiteOur findings may be partly related to testing patients OFF medication, which produces greater disturbances in brain activation during response inhibition than when patients are tested ON medication . HoweverAnother limitation is that participants attained a higher educational level than is typical, so that the results may not generalize to all PD patients. Our recruitment methods were not biased toward highly educated individuals, as we recruited individuals throughout the San Diego area . It is possible that greater cognitive reserve helps people to better cope with brain pathology by sustaining normal performance levels on the inhibition task . To fullOur results demonstrate that functional reorganization of inhibition regions in PD precedes the development of inhibition deficits and clinically significant cognitive impairment. Functional reorganization was typified by abnormally enhanced long-range connectivity rather than altered within-network connectivity, which was preserved. Successful inhibition was related to strengthened connectivity of executive  and action planning (preSMA) centers, largely with attention-related networks that have been the focus of some behavioral interventions for deficient inhibitory control. Amplified interactions of inhibition regions with attention networks may help sustain inhibitory control by postponing the onset of cognitive decline, although other explanations are possible. Unlike controls, PD participants also did not make use of the hyperdirect pathway or basal ganglia, to stop ongoing actions, possibly owing to dopaminergic cell loss. As disease severity progresses, one might expect that difficulties engaging these key subcortical pathways would lead to the development of response inhibition deficits. Interestingly, despite no group differences in tissue diffusivity or posterior cortical volume, individual differences in these structural variables correlated in a meaningful manner with some aberrant functional interactions in PD. These findings suggested that subtle decreases in underlying tissue diffusivity or brain volume may, respectively, undermine the enhancement of normal cortical-striatal connectivity or cause a strengthening in cortical-cortical connectivity. Lastly, context-dependent connectivity measures were highly accurate in distinguishing patients from controls, suggesting that they may be early markers of neuronal changes. Longitudinal studies are needed to directly test the above proposals.DLH designed the study, conducted statistical analyses, interpreted the data, and assumed the lead role in writing the paper. She is the principal investigator of the primary Department of Veterans Affairs Merit Award. QS analyzed the task-activated fMRI data, consulted on the anatomical MRI analyses, and reviewed the manuscript. RJT analyzed the diffusion tensor imaging data and reviewed the manuscript. GNC contributed to the project design, collected the data, consulted on the analyses of task-activated fMRI and anatomical MRI data, and reviewed the manuscript. IL contributed to conceptual discussions about the study results and edited important intellectual content of the paper. JVF contributed to data collection, conceptual discussions about the study results, and edited important intellectual content of the paper. MH consulted on the diffusion tensor imaging analyses and edited important intellectual content of the paper. RRL consulted on the anatomical MRI analyses and edited important intellectual content of the paper.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.


The results were confirmed by Western blotting and complemented with corresponding experiments in HUVEC cells. A strong positive and statistically significant correlation between the mean inhibitory effects of the tested polyphenols on both Akt-residues Ser473 and Thr308  was determined by immunoblotting. Interestingly, the structural characteristics favoring pAkt inhibition partially differed from structural features enhancing the compounds’ antioxidant activity. The present study is the first to quantitatively compare the influence of polyphenols from nine different structural subclasses on pAkt in endothelial cells. These effects might be advantageous in certain T2DM-complications involving over-activation of the Akt-pathway. The suggested molecular mode of action of polyphenols involving Akt-inhibition contributes to understanding their effects on the cellular level.Polyphenols exert beneficial effects in type 2 diabetes mellitus (T2DM). However, their mechanism of action remains largely unknown. Endothelial Akt-kinase plays a key role in the pathogenesis of cardiovascular complications in T2DM and therefore the modulation of its activity is of interest. This work aimed to characterize effects of structurally different polyphenols on Akt-phosphorylation (pAkt) in endothelial cells (Ea.hy926) and to describe structure-activity features. A comprehensive screening via ELISA quantified the effects of 44 polyphenols (10 µM) on pAkt Ser473. The most pronounced inhibitors were luteolin (44 ± 18%), quercetin (36 ± 8%), urolithin A (35 ± 12%), apigenin, fisetin, and resveratrol; ( Polyphenols are plant secondary metabolites which are ubiquitously present in human food, such as fruits, vegetables, nuts, spices and beverages . These sO-glycosides, sulfates, and glucuronides  and M2 [δ-(3-methoxy -4-hydroxyphenyl)-γ-valerolactone] were synthesized by M. Rappold and kindly provided for use.−2 M) of the polyphenols in DMSO were prepared and stored at –80 °C or directly used for cell culture experiments.Stock solutions , 1 mM sodium pyruvate, and a mixture of 100 U/mL penicillin with 100 μg/mL streptomycin (1% Pen/Strep) at 37 °C in 5% CO2 atmosphere. Briefly, cells were seeded into 75 cm2 flasks  at a density of 2000–2500 cells/cm2. The medium was changed every second day. When cells reached 90% confluence they were passaged. For this purpose, they were rinsed three times with warm Dulbecco’s phosphate buffered saline (PBS) and treated with trypsin/EDTA solution 1× . Detached cells were resuspended in a fresh warm medium. Cell suspension was either seeded into flasks for further cultivation or into 6-well plates at a density of 0.3 × 106 cells per well for experiments.The immortalized human endothelial cell line Ea.hy926 was generously provided for use from Dr. C.J. Edgell  . Ea.hy923, 20% heat inactivated FBS, 2 mM l-glutamine, 1% low serum growth supplement (LSGS), and 1% Pen/Strep was used. All flasks and wells used for HUVECs were coated with a sterile 1% gelatin solution. After trypsinization cells were mixed with growth medium and pelleted by centrifugation  in order to remove the proteolytic enzyme. Then the cells were resuspended in a fresh warm M 199 and passaged or seeded for an experiment as indicated. Trypan blue staining was routinely performed to determine the vitality of cells.Primary endothelial cells HUVEC were cultivated according to the protocol described above with some deviations. Instead of DMEM, M 199 (M3769) supplemented with 4500 mg/L glucose , 2.2 g/L NaHCOCells were grown in a 3 mL medium/well until they reached confluence. Afterwards, they were incubated with polyphenolic compounds at a concentration of 10 µM, which was considered as a physiologically relevant concentration . Due to ® BCA Protein Assay Kit) according to the protocol provided by the manufacturer .After treatments cells were rinsed twice with ice-cold PBS and 300 µL precooled lysis buffer , supplemented with phosphatase and protease inhibitors , were added to each well. Cellular proteins were extracted at 4 °C on a shaker for 15 min. Cell residues were scraped off the wells and the lysates were transferred to 1.5 mL precooled plastic tubes and centrifuged at 4 °C for 10 min at 18,000 g. The supernatants were transferred into fresh tubes, vortexed, and briefly sonicated. If needed, protein concentration in extracts was determined via BCA assay (Pierce® Human/Mause/Rat Phospho-Akt (S473) and Total Akt ELISA Kit  was used according to the manufacturer’s protocol with some deviations. Lysates were diluted 1:3 with (1×) assay diluent and were added to assay wells and incubated overnight at 4 °C. In each experiment, the lysate from DMSO-treated cells (corresponding to the 100% control) and its 1:1 dilution (50% control) were used as reference standards. The antibody detecting pAkt (Ser473) was diluted 1:55 in (1×) assay diluent as suggested by manufacturer, while pan  Akt antibody was diluted 1:220 in order to avoid readouts outside the linear range of the assay. Based on optical density readings after blank subtraction, values (in %) for pAkt (Ser473) and pan Akt were calculated using the reference standards. Then data for pAkt (Ser473) were normalized with reference to pan Akt.For the screening of pAkt, RayBioFor Western blot, samples were mixed with Laemmli buffer (4×) and DTT (dithiothreitol). After incubation for 7 min at 70 °C under shaking (1000 rpm) they were vortexed, shortly spun and either directly analyzed or stored at –20 °C. Western blot was performed with phosphospecific antibodies against pAkt Ser473 (dilution: 1:1000) and pAkt Thr308 . Pan Akt (1:2000) was used as a loading control. ®, Gel-Electrophoresis Equipment, Bio-Rad Laboratories, Inc., Grand Junction, CO, USA) using 5% stacking and 10% resolving polyacrylamide gels (Rotiphorese Gel 30 (37.5:1) from Carl Roth GmbH + Co, Karlsruhe, Germany). Gels were loaded with equal protein concentrations (20–30 µg/lane). Proteins were subsequently transferred onto nitrocellulose membranes using wet blotting . The process took one hour and was performed at 4 °C and 375 mA/100 V.Proteins were separated by SDS PAGE (Mini-PROTEAN Tetraw/v) SDS, 1.0% (v/v) Tween 20 in Millipore water, pH = 2.2) was used.Membranes were blocked for one hour at room temperature using 5% BSA in TBST  in case of phospho-Akt (Thr308 and Ser473) and 5% low-fat dry milk powder  in TBST for pan Akt membranes. After a brief wash with TBST, primary rabbit antibodies in 5% BSA-TBST were applied and incubated at 4 °C overnight on a shaker. To remove the unbound primary antibodies, membranes were washed four times for 10 min with TBST. A secondary, HRP-linked anti-rabbit antibody (dilution: 1:10000) was applied for two hours at room temperature . To reduce signal/noise ratio, membranes were again washed four times for 10 min with TBST. A chemiluminescent detection  using the FluorChem FC2 Doku imaging system  was performed. The images were quantified densitometrically using of ImageJ . After d®, Version 2010. For multiple comparisons, analysis of variance (one-way ANOVA) was utilized. The statistical significance of the differences between two samples was examined using a two-tailed paired Student’s t-test. The significance level α was set to 0.05. The tests were performed with the Real Statistics Add-in©  of Excel [Mean values, standard deviations (S.D.), medians, and mean deviation (Mean Dev.) were calculated with Microsoft Excelof Excel . Alternaof Excel .The comprehensive screening regarding short-term effects of polyphenols on the phosphorylation of Akt at Ser473 included 44 compounds with different structures and certain microbiota-generated metabolites .Overall, 26 compounds revealed some inhibitory potential. Among them, 11 substances showed pronounced (> 20%), 10 compounds lower, but still distinguishable (between 10% and 20%), and five compounds weak (less than 10%) inhibitory activity. The other 18 polyphenols did not inhibit pAkt . The inhFlavones and flavon-3-ols caused the clearest inhibition of Akt-phosphorylation . The mos(+)-Catechin, (−)-epicatechin, ferulic acid, M2, naringenin and all investigated glycosides  exhibited a non-statistically significant tendency to slightly augment pAkt Ser473.p = 0.001) were determined by one-way ANOVA with the Tukey HSD test [p = 0.008; Statistical significant effects of luteolin, urolithin A, apigenin, quercetin, fisetin, and resveratrol  > apigenin (p-OH) > chrysin (Ø OH); 3,4′-dihydroxyflavone (p-OH) > 3-hydroxyflavone (Ø OH). The investigated glycosides showed no effects on Akt-phosphorylation. This modification abolished the inhibitory potential as seen when comparing apigenin (32 ± 6%) compared to vitexin (−8.5%), baicalein (18%) compared to baicalin (−11%) and wogonoside (−5%).Several molecular sites of the polyphenols appeared to be important for the inhibition of Akt-phosphorylation. The presence of a double bond C2=C3 (ring C) appeared to be essential for the inhibitory activity of flavones and flavon-3-ols. This was concluded from the comparison of the inhibitory effects caused by quercetin (36 ± 8%) compared to taxifolin (9%), and apigenin (32 ± 6%) compared to naringenin (−7%). Hydroxylation at C3 in the C-ring reduced the inhibitory potential of flavones/flavon-3-ols. Evidence for that were the differences between the effects of luteolin (44 ± 18%) compared to quercetin (36 ± 8%), and apigenin (32 ± 6%) compared to kaempferol (17 ± 12%). Methylation of the hydroxyl group (C3) partially prevented the decrease of the inhibitory activity as seen by comparing the effects of 3-methoxyflavone (13%) compared to 3-hydroxyflavone (6 ± 4%). Compounds featuring a single hydroxylation or methylation at C6-position in the A-Ring also possessed distinguishable inhibitory activities (>20%: 6-hydroxyflavone and 6-methoxyflavone). On the other hand, the most active flavones  were hydroxylated at the C5- and C7- position of the Ring A, which suggested that these structural features were important for the inhibitory potential. Since the Ring B of the most active flavones/flavon-3-ols  was hydroxylated, the OH groups (Ring B) obviously contributed to the inhibitory effects. The presence of a meta- and a para-OH groups appeared to be optimal for the activity as seen for luteolin  > pinostilbene (19 ± 14%) > pterostilbene (−6 ± 1.6%) ≈ 3,4′,5-trimethoxy-trans-stilbene (0.9 ± 10%)).In the subclass of urolithins clear differences in their inhibitory effects were observed. As seen with urolithin A, two OH-groups at the C3 and C8 positions and lack of further substituents were important for the activity. Thus, only minor changes, such as an addition or elimination of a hydroxyl group were responsible for a remarkable change in the inhibition. Similar observation was valid for flavonols: Active quercetin and slightly active morin – analogs, differing only by the position of one phenolic OH-group in the B-ring.To determine whether structural determinants responsible for the observed inhibitory effects on pAkt in the present study matched published structural features enhancing the polyphenols’ antioxidant properties , both acp = 0.001 **). Other urolithins  showed no statistically significant inhibitory effects on Akt-phosphorylation and were not further investigated -catechin and ellagic acid were compared with their corresponding intestinal microbiota-generated metabolites regarding their in vitro inhibitory potential on pAkt Ser473. (+)-Catechin caused a slight statistically non-significant increase of Akt-phosphorylation with 9 ± 6% , while M1 -γ-valerolactone) exhibited no influence on pAkt (n = 1), and the methylated M2 (δ-(3-methoxy-4-hydroxyphenyl)-γ-valerolactone) tended to increase pAkt with 9 ± 9% (n = 3). This effect was not statistically significant and was not further investigated , panel An = 1–2, , panel BTo confirm results from the ELISA analysis, the short-term effects of eight selected polyphenols from five different structural subclasses  were re-p < 0.01, one-way ANOVA with Tukey HSD test) of Akt-phosphorylation level after short-term incubation (5 min) compared to controls. The compounds with the most prominent inhibitory effects on pAkt Ser473 (mean inhibition ± standard deviation) were quercetin (40 ± 5%), luteolin (30 ± 11%), resveratrol (26% ± 6%) and apigenin (22.6 ± 10%). In contrast, genistein (7 ± 19%), 3,4′,5-trimethoxy-trans-stilbene (3-MS) (8 ± 14%) caused only a small and statistically non-significant reduction in the phosphorylation of Akt at Ser473 compared to the negative control. Taxifolin (0.6 ± 18%) did not reveal any activity  and 89 ± 5% (n = 3), respectively. The inhibitory effect of polyphenols on pAkt Thr308 was similar for quercetin (36 ± 3%), luteolin (27 ± 10%), resveratrol (32 ± 13%) and apigenin (28 ± 10%). A minor, not statistically significant increase in Akt-phosphorylation at Ser473 was observed after incubation with (+)-catechin. Likewise, genistein, taxifolin, 3,4′,5-trimethoxy-trans-stilbene and (+)-catechin showed minor and no statistically significant effects on Thr308 .p < 0.01) and very similar to the effects determined in experiments employing the immortalized cell line Ea.hy926  were analyzed by Western blot. This model compound reduced the phosphorylation of Akt at Ser473 site by 44 ± 5%. This effect was statistically significant (Ea.hy926 .2 = 0.898) indicating a strong positive and statistically significant (p = 0.0003) correlation between both variables  and resveratrol (26 ± 6% compared to 26 ± 5.6%). Bigger differences were observed for luteolin (30 ± 11% compared to 44 ± 18%) and apigenin (22.6 ± 10% compared to 32 ± 6%). However, Western blot analysis is a complex procedure and multiple steps  can contribute to variations of results. This technique is generally considered as a semi-quantitative method [To the best of our knowledge, the present study is the first comprehensive screening for the effects of polyphenols on pAkt in endothelial cells. The scope of the present study was broad, including representatives from chemical classes such as stilbenoids, urolithins, and phenolic acids, and investigating the effects of methylation and glycosylation. As far as we know, 6-hydroxyflavone, 6-methoxyflavone, 7-methoxyflavone, and pinostilbene have not previously been described as inhibitors of Akt-phosphorylation.In another study, 24 flavonoids were investigated in adipocytes by Western blot regarding their activities on multiple targets related to insulin-signaling, among which was Akt-kinase . The autIn a related study employing macrophages the effects of stilbenoids and their semi-synthetic derivatives on the PI3K/Akt-pathway were examined by immunoblotting . PiceataAs Akt is considered as a major downstream effector of PI3K , publicaCompared to most previously published papers, both phosphorylation sites of Akt (Ser473 and Thr308) were investigated in the present study, as both sites are important for the full activation of this enzyme . The strA bio-activation of ellagic acid was shown for different targets such as heme peroxidases  and estrDetermination of semi-quantitative structure-activity relationships of the polyphenols and their effects on Akt-phosphorylation revealed that the most active compounds belonged to the subclass of flavones, followed by flavonols. For these two groups the structural hallmarks were the double bond between C2 and C3 of the ring C, preferably lack of substitution at the C3 of the ring C, and 3′,4′-catechol group in the ring B. The most active compounds were those with unsubstituted OH-groups (no methylation or glycosylation).The phosphorylation status of Akt was reported to be dependent on oxidative stress levels . TherefoModerate inhibitory effects of polyphenols as observed in the present study might be beneficial in the case of endothelial dysfunction. It has been described that the hyperactive S6K1  in senescent endothelial cells might contribute to an increased oxidative stress and decreased NO levels. S6K1 is a downstream target of Akt and its over-activation was reported to contribute to insulin resistance . It was The present study for the first time quantitatively compared the influence of polyphenols from nine different subclasses on Akt-phosphorylation in endothelial cells. Quercetin, resveratrol, apigenin and luteolin statistically significantly inhibited the phosphorylation of both Akt Ser473 and Akt Thr308. A differential inhibitory effect on Akt-phosphorylation for urolithin A, but not for other structurally related compounds was uncovered. A semi-quantitative structure-activity analysis suggested functional groups important for the inhibitory activity of polyphenols on Akt-phosphorylation. It was hypothesized that PI3K-inhibition, but not solely the antioxidant properties of those polyphenolic compounds might play a major role for their effects on the Akt-kinase.


Scutellaria baicalensis shoots and its eight high content flavonoids were investigated for their inhibitory effects against α-glucosidase and α-amylase. Results show that abilities of the extract in inhibiting the two enzymes were obviously higher than those of acarbose. Moreover, inhibitory abilities of all the eight individual flavonoids against the two enzymes show exactly a same order , and their structure–activity relationship could be well-interpretated by the refined assign-score method. Furthermore, based on the inhibitory abilities and their contents in the extract, it was found that the eight flavonoids made predominant contributions, among which baicalein and scutellarin played roles as preliminary contributors, to overall inhibitory effects of the extract against the two enzymes. Beyond these, contributions of the eight flavonoids to the overall enzyme inhibitory activity were compared with those to the overall antioxidant activity characterized in our recent study, and it could be inferred that within the basic flavonoid structure the hydroxyl on C-4′ of ring B was more effective than that on C-6 of ring A in enzyme inhibitory activities while they behaved inversely in antioxidant activities; scutellarin and apigenin contributed more to the overall enzyme inhibitory activity, and baicalin and scutellarin, to the overall antioxidant activity of the extract; and flavonoids of the extract, apart from directly inhibiting enzymes, might also be conducive to curing type 2 diabetes via scavenging various free radicals caused by increased oxidative stresses. A flavonoids-rich extract of  Diabetes is a chronic disease caused by deficiency in and insensitivity to insulin , usuallyA promising approach for management of diabetes, particularly type 2 diabetes, is to decrease postprandial hyperglycemia by inhibiting carbohydrate hydrolyzing enzymes in gastrointestinal tract . α-AmylaActinoplanes) that potently inhibits in vitro and in vivo such brush-border enzymes as glucoamylase, dextrinase, maltase and sucrase as well as the pancreatic α-amylase  × C × 100, where Ei and E0 are enzyme inhibitory effects of an individual flavonoid and the flavonoids-rich extract, respectively, on the base of acarbose equivalent, and C is the content of an individual flavonoid in the extract in mg/g. Table O-glucuronide > chrysin-7-O-glucuronide > isocarthamidin-7-O-glucuronide  were the same, thus differences occurred only with the other three, namely, scutellarin > apigenin > baicalin in enzyme inhibitory, and baicalin > scutellarin > apigenin in antioxidant abilities, indicating that scutellarin and apigenin contributed more to the overall enzyme inhibitory ability, and baicalin and scutellarin, to the overall antioxidant ability of the extract.By comparing the contributions to overall enzyme inhibitory effects of the flavonoids-rich extract demonstrated by the current study with those to overall antioxidant activities reported in our previous work , we founS. baicalensis shoots showed high inhibitory effects against both α-glucosidase and α-amylase  and B (C-4′) were augmentors, and sugar moiety was an attenuator influencing enzyme inhibitory capacity. In addition, we found that the eight flavonoids made contributions of 61.95 and 64.16% to overall activities in the two assays, respectively. Among the eight flavonoids, baicalein and scutellarein were not only the higher content components but the superior contributors. Accordingly, the eight high content flavonoids were the predominant contributors, and baicalein and scutellarein were defined as the primary contributors in the flavonoids-rich extract from S. baicalensis shoots. Furthermore, by comparing these results with those in our previous study [S. baicalensis shoots as potential supplements for various functional foods.In the present study, flavonoids-rich extract from us study , it was


The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies. Primary microseism has a spectral content equal to the ocean wave frequency (period between 13 and 20 s) and its source is associated with the energy transfer of ocean waves breaking/shoaling against the shoreline2. Secondary microseism, with most energy between 5 and 10 s (roughly twice the frequency of ocean waves)5, is generally characterised by higher amplitude than primary microseism. According to the most accredited theories, secondary microseism is generated by interactions between waves of the same frequency travelling in opposite directions6. Finally, there is a short period secondary microseism, characterized by period shorter than 5 s and sources generally linked to local sea state and wave activity, and influenced by local winds8.Modern seismology is able to obtain plenty of information by the analyses of signals, that until a couple of decades ago were considered to be noise, such as microseism. This, considered as the most continuous and ubiquitous seismic signal on Earth, is generated by ocean wave energy coupling with the Earth’s ground10 and the detection of seismic velocity variations in both volcanic and tectonic area12. Furthermore, because of the microseism source nature, such a signal has been used to make inferences on climate changes15.Microseism studies have today broad applications, such as the reconstruction of crust and upper mantle by noise tomography14. However, such a pattern is different in Antarctica where during the winter, because of the sea ice, the oceanic waves cannot efficiently excite seismic energy18. Although the link between microseism and ocean wave parameters has been quantitatively explored20, the relationship between sea ice concentration and microseism recorded in Antarctica has only been qualitatively treated. An exception is the investigation performed by18, who quantitatively studied such a relationship only in the Antarctic Peninsula. In addition, to date no technique has been found to try to infer the sea ice distribution in the Southern Ocean, based on microseism recordings.Microseism amplitudes at temperate latitudes in both northern and southern hemispheres show strong annual periodicity with maxima during the winter seasons, when the oceans are stormier, and minima during summers21, remote sensing22, volcano monitoring23 and earthquake studies24.Such investigations, that have to face the processing of decade-long seismic signals as well as the lack of precise models dealing with the multi-dimensional complexity inherent in the data of such extreme environments, are really challenging. The modern data mining and machine learning techniques can help extract implicit useful information and knowledge from a large quantity of data, allowing to unravel hidden relationships between parameters. Recent applications of data mining and machine learning techniques to geosciences have regarded many topics such as seismic signal classification2 or to local nearshore wave-wave interaction (short period secondary microseism)7 . Although the considered time interval ranges from 1993 to 2017, the temporal coverage of the acquired data significantly varies from station to station.All the available seismic data, recorded by the vertical component of 20 stations from 1993 to 2017, were used. These stations, chosen because located close to the Antarctica grounding lines  and then to those microseism sources, mainly related to the energy transfer of ocean waves breaking/shoaling against the shoreline (primary microseism)Once the data were downloaded, they were corrected for the instrument response, and spectrograms Fig.  and RMS 17, who analysed a shorter time period between 2007 and 2012, and interpreted this feature as due to the circumpolar westerlies, making Drake Passage and Bellingshausen Sea very stormy. Strong microseism sources located in this area have also been reported in other papers26. Moreover, the maps, displaying the spatial distribution of the median value of RMS amplitudes in the three investigated period bands  during February–April  at all the considered stations Fig. . Moreoveril Fig.  and Octoril Fig. , confirm2 cell of a grid comprising the entire Antarctic polar sea ice cover, are obtained by brightness temperature data27. Such data were downloaded as GeoTIFF files, providing the daily sea ice concentration data in a georeferenced format. In particular, the version 2.1 files were used for the period 2000–2016, the version 3.0 files for the period 1993–1999 and 2017. The two versions have no difference in terms of daily sea ice concentration28. The sea ice concentration is represented with a scale ranging from 0 to 1000. We divided this value by 10 to get data in percent. It has to be noted that values lower than 150 (15%) are considered statistically irrelevant because of instrumental limits. Such a parameter is affected by larger uncertainty during summer, because of the thinner sea ice29.The microseism RMS amplitudes within the three period bands were compared by Spearman correlation coefficient to the sea ice concentration on the whole Southern Ocean. Information about temporal and spatial variability of sea ice concentration, defined as the percentage of ice cover within each 25 × 25 kmThe results of the correlation analysis highlight clear anti-correlation patterns for all the bands Figs , S3a,c. 18, in regions with strong anti-correlation we expect that sea ice interferes with the microseism generation.In addition, to seek a prospective spatial dependence between sea ice concentration and the location of the station recording microseism, the long-period components  were filtered out and the correlation analysis was performed again between the filtered time series Figs . The corIn a few cases, it is also possible to note low values of Spearman correlation coefficient in areas far away from the stations recording the microseism. This is especially evident for stations located on or nearby the Antarctica Peninsula, such as ORCD, ESPZ and DNTW see Figs  and 4b, 30 to build an equation linking sea ice and microseism amplitudes in Bering Sea.To verify the dependence of the anti-correlation from the distance between the sea ice and the seismic station recording microseism, a cumulative 3D density plot of all the correlation maps was obtained per each period band, showing the distance in the x-axis, the Spearman correlation value in the y-axis, and the number of Spearman correlation estimations, performed on the filtered deseasonalized time series, with the color scale Figs  and S7a.The observed decrease in sea ice sensitivity of microseism, due to the increasing distance from the seismic station, paves the way to implement an algorithm to spatially and temporally reconstruct the sea ice distribution around Antarctica on the basis of the microseism amplitudes. However, to do that, an analytical approach, based on microseism wave propagation, seems to be impracticable for the few and sparse data available in a highly heterogeneous and complex environment that would conduct to a strongly underdetermined ill-posed inversion problem. For this reason, we exploited the capabilities of the newest regression algorithms in machine learning to reconstruct the sea ice field starting from the knowledge of the microseism features or their transformations. In particular, the method we used is composed of three main steps 32, Time Smoothing (SMT). By using them, we obtained several potential sets of input features (hereafter referred to as IF) for machine learning modeling. In addition to the transformed microseism data, also a time-related feature, defined as a sinusoidal oscillator between 0 and 1 with annual period, and with 0 corresponding to the time with the maximum peak of the sea ice extent (defined as the measurement of the area of ocean where there is at least 15% of sea ice concentration), was introduced.As for the step (i), to exploit the maximum information content of the microseism data, we applied the following transformations on all the RMS amplitude time series: probability integral transformation (PIT)33, Random Forest Regression34, K-Neighbors regression35, and Extremely Randomized Trees Regression36.Concerning the training step (ii), we exploited the potentiality of machine learning techniques (hereafter referred to as MLTs) to build a regression model able to predict the sea ice concentration from microseism-related features. In particular, we tested the following supervised machine learning techniques: Linear Regression37. The performance indexes used to compare the models were the mean absolute error (MAE) between the observed sea ice concentration and the predicted one, and the percentage (ϕ) of the cells in the sea ice grid, showing an absolute error  lower than 50% (threshold chosen to discriminate gross errors).Finally, regarding the last step (iii), we evaluated the unbiased generalization capacity of each pair MLT/IF by calculating the prediction performance through K-fold cross-validationσMAE)  Table , we founE) Table  is not a18 and for this reason it is characterized by almost permanent sea ice In summary, we quantitatively investigated the relationship between microseism recorded along the coasts of Antarctica and the sea ice concentration in the whole Southern Ocean.20. Indeed, if we assume that sea ice concentration is a proxy of sea ice strength30, the increase in sea ice concentration: (i) prevents swell from reaching the coast, decreasing the efficiency of primary microseism generation and (ii) inhibits the swell reflection along the coast, reducing the secondary microseism energy16. In particular, as for the short-period secondary microseism, such a band is likely to be generated by local nearshore wave-wave interaction7, heavily modulated by the presence of sea ice.Clear anti-correlation patterns between microseism and sea ice concentration were found at all the investigated microseism bands Figs  and S3. The microseism bands, showing the strongest link with sea ice, are primary and short-period secondary microseism Fig. , corrobo30, such a distance could be related to the attenuation length. Indeed, taking into account the entire period band of microseism (from 20 s to 2.5 s), attenuation coefficient of ~10−3 km−1 can be found in literature44. Hence, the corresponding attenuation lengths turn out to have the same order of magnitude as the estimated maximum distance of influence of sea ice on microseism. Moreover, this distance could also be linked to the temporal variability of sea ice extent in the coast nearby the station. Indeed, the average value of the difference between maximum and minimum linear extent of the sea ice, measured approximately normal to the coastline closest to the station during September 2014 and March 2017 , its sources are mostly located at even shallower depths (e.g.45). If we consider the 150 m depth, this limit corresponds to distances from the Antarctic coastline much shorter than 1,000 km46.In addition, we clearly show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to disappear for distances above 1,000 km Figs  and S7. 42). This study does not constrain the locations of the microseism sources, their amplitudes and the corresponding decay with distance, but rather suggests that sea ice with maximum distance of 1,000 km, on average, contributes to modulate the microseism amplitude. Indeed, location of microseism sources is a hard task, as microseism signals are non-impulsive, and the sources are generally diffuse and variable in time18. Hence, the classical location algorithms, used in earthquake seismology and based on the picking of the different seismic phases, cannot be applied to locate microseism sources. Array processing techniques, that can overcome the above-mentioned difficulties, have provided locations of microseism source areas surrounding Antarctica26. However, as the array data available in Antarctica are sparse, the microseism array locations have been obtained only for short time intervals (for instance a couple of months in26). Preliminary information about the direction of the microseism sources, with less accuracy than by using array data, can also come from polarization analysis of single 3-component station25.It is worth noting that such a 1,000 km-threshold, that has to be considered as an average value among the different stations, does not signify that microseism recorded in Antarctica cannot have also distant sources, as highlighted by several authors . In addition, it is also possible to observe peculiar patterns of specific stations, such as ESPZ and ORCD. For these two stations, the duration of the time interval characterised by microseism RMS amplitude minima is shorter compared to the others, likely reflecting the shorter-lived effect of sea ice modulation on microseism at the relatively lower latitudes of ESPZ and ORCD.The microseism sensitivity in sea ice is reflected in the slightly different annual patterns of RMS amplitude time series observed among the stations Fig. . Such diFinally, we propose an innovative method, based on up-to-date machine learning techniques, able to reconstruct the spatial-temporal distribution of sea ice concentration by using microseism recorded in different period bands by distinct seismic stations. The importance of each station in the prediction of sea ice concentration was evaluated Fig.  and turn27, are not available, with wide applications in many fields, first of all the climate studies.The quality of the modelling results, obtained by the machine learning techniques for the relatively small dimension of measured data, indicates that microseism signal carries significant information about the surrounding sea ice concentration. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring48. In the light of this, also the wavefield features 49 could add additional information to the machine learning modelling, if included as further inputs. The inclusion of these new inputs will likely improve the capability to reconstruct the spatial-temporal distribution of sea ice concentration around Antarctica by the microseism.A future development of this study will be the inclusion of time series of horizontal seismic component amplitudes as input in the machine learning modelling. While the vertical component mainly brings information about Rayleigh waves composing microseism, the horizontal components allow taking into account both Rayleigh and Love waves. Indeed, it has been highlighted by recent papers that the contribution of Love waves in microseism can be significant50, and visualised as spectrograms  of the data recorded by the vertical component of the 20 stations shown in Fig. 18. The RMS amplitude time series were obtained, gathering median daily RMS amplitudes, computed on values calculated over consecutive 81.92-sec-long windows  amplitudes, three distinct period bands were taken into account: primary microseism (13–20 s), secondary microseism (5–10 s), and short period secondary microseism (2.5–5.0 s)ows Fig. .Successively, the RMS amplitude time series were smoothed by a 90-day-long moving median, split in year-long windows, that were stacked and normalised by subtracting the minimum value and dividing by the maximum value Fig. . Then, a51, a map showing the spatial distribution of RMS amplitude values was plotted per each period band . The same technique was applied to generate maps focused on the periods with the maximum , were obtained and compared with the RMS amplitude patterns by Spearman correlation coefficient. This coefficient, defined as a nonparametric measure of rank correlation, was preferred with respect to the more widely used cross correlation coefficient, whose application is limited to explore linear dependence between normally distributed parameters54. Then, spatial distributions of the Spearman correlation coefficient in the portion of Southern Ocean, seasonally covered by sea ice, were obtained  we computed smoothed time series of RMS amplitude and sea ice by using a 15-day-long moving median, and subtracted from each value of the original RMS amplitude and sea ice series, the value of the smoothed series shifted back by one year; (ii) we divided the RMS amplitude and sea ice time series into 1-year-long windows, stacked them and smoothed them by using a 15-day-long moving median, and then we subtracted from each sample of the original time series the corresponding sample of the yearly stacked series. By using both methods, we obtained very similar time series. Also in this case, we obtained maps of spatial distribution of Spearman correlation coefficient Figs .18).To test whether the observed values of Spearman correlation coefficient are significantly different from zero or not (null hypothesis), the t-test, taking into account the different number of samples in the distinct RMS amplitude time series, was performed and the space distribution of p-value  was calculated; in particular, p-values lower than the significance level of 0.05 were considered sufficient to reject the null hypothesis Fig. . Such a Focusing on the filtered RMS amplitude and sea ice time series, we collected all the Spearman correlation values and the corresponding distances for all the stations and the three period bands. A cumulative 3D density plot was obtained per each period band, showing the distance in the x-axis, the Spearman correlation value in the y-axis, and the normalised number of Spearman correlation estimations with the color scale Figs  and S7a.The method is composed of three steps Fig. : (i) dat31): to maximize the distribution regularity of the data in a range. The technique attempts to convert a random variable with any given continuous distribution into a random variable having a standard uniform distribution55.Probability Integral Transformation (PIT32): to maximize the class-separability of clustered data. The goal of LDA is to project the dataset onto a lower-dimensional space with better class-separability. To define the classes of sea ice space distribution, we firstly found the best number of clusters that can separate the sea ice space distribution images by using the Calinski-Harabasz56 and the Silhouette57 indexes in the range between 2 and 365. We found the optimum number of 32 different classes for the sea ice space distribution dataset, that we clusterized by k-means algorithm58. Then, we applied the LDA to the seismic features corresponding to these classes. With an explained variance greater than 95%, we found the transformed and reduced seismic features that better separate the sea ice space distribution classes.The Linear Discriminant Analysis (LDATime Smoothing (SMT): to reduce the time variability of the seismic data, we applied a sliding window smoothing the signal. The mean autocorrelation function of the RMS amplitude time series shows a fast decay of more than 60% after only 2 days, thus we considered a causal-consistent smoothing window of 3 days.As for the step (i), the time intervals characterized by an almost complete coverage of seismic data from 18 out of 20 stations were selected (2011–2012 and 2016–2017), and the corresponding RMS amplitude data were extracted. SYO and DNTW stations were not used because of the total lack of data during the most recent time interval (2016–2017). We applied the following transformations on all the RMS amplitude time series:As potential input features (IF) for machine learning modeling, we tested all the possible ordered selections without repetition of any subset (included the empty one) of the described transformations  on the RMS amplitude data. To be sure that the model predicted the sea ice concentration from the information carried by microseism signals and not from the implicit seasonality, we compared the results obtained by using only a variable linked to the period of the year as input , the supervised machine learning techniques (MLT), taken into account to predict the sea ice concentration, were:37. We considered K = 10 with partially overlapped subsets of 365 timely-consecutive daily data. The choice to consider 365-consecutive daily data for testing was due to the slow dynamics of sea ice formation and melting. In a completely random selection of the test set, results could be optimistically biased for the presence of close-in-time (and hence similar) patterns in the learning set. Considering an annual (i.e. 365 daily data) test set, we avoided biases in results due to such seasonal correlations. The performance indexes used to compare the models were:The mean absolute error (MAE) between the observed sea ice concentration and the predicted one, calculated in a mask where the ice concentration is not null for the whole period.The percentage (ϕ) of the cells in the sea ice grid, showing an absolute error, defined as the absolute value of the difference between predicted and true sea ice concentration, lower than 50% (threshold chosen to discriminate gross errors).The step (iii), cross-validation, consisted of evaluating the unbiased generalization capacity of each pair MLT/IF by calculating the prediction performance through K-fold cross-validationMAE + σMAE. The best performance was obtained by using the technique of Extremely Randomized Trees on RMS data sequentially post-processed by PIT, LDA and SMT with the addition of the time-related feature.Cross-validation allowed us to estimate the unbiased mean and standard deviation (σ) of the performance indexes. Table 59. However, the optimal model uses the LDA data transformation which projects the original data into a new linear space. Hence, to reconstruct the importance of the original inputs, we had to back-project the importance of the surrogate features to the original space through the normalized eigenvectors used in the transformation (Fig. The Extremely Randomized Trees approach has the advantage to easily supply an index of input importanceion Fig. . Figure Supplementary figures and table