Eleven Saracatinib manufacturer healthy subjects (6 males; 5 females) participated in the current study (age: 27.4 ± 7.8 years; mass: 72.0 ± 13.4 kg; height: 1.76 ± 0.08 m). All participants were free of lower extremity injury at the time of testing and had no history of major lower extremity injury or neurological disorder. All participants signed an informed consent

statement approved by the Institutional Review Board prior to participating in the study. Each participant performed five level walking trials across a 10-m walkway in each condition (Fig. 1): normal shoes, Gait Walker short-leg walker (DeRoyal Industries, Inc., Powell, TN, USA) and Equalizer short-leg walker (Royce Medical Co., Camarillo, CA, USA). Preferred walking speed was determined using a pair of photocells (1000 Hz, 63501 IR, Lafayette Instrument Inc., Lafayette, IN, USA) from three walking trials at a self-selected speed in a randomly selected walker.4 Photocells were placed 1.5 m before and after the force platform and were approximately shoulder height. Walking speed was monitored and maintained within 10% of Selleck SCH772984 the self-selected speed during the data collection. The walker conditions were randomized and followed by the lab shoe condition. An EMG system (600 Hz, Noraxon USA, Inc., Scottsdale, AZ, USA) and force platform (1200 Hz,

American Mechanical Technology Inc., Watertown, MA, USA) were used to simultaneously collect surface EMG (sEMG) and ground reaction forces from the right limb during walking trials. Surface electrodes were placed over the muscle belly of the m. Tibialis Anterior (TA), m. signal peptide Peroneus Longus (PL) and medial head of the m. Medial Gastrocnemius (MG). The skin beneath the

electrodes was shaved, cleansed and abraded to minimize skin resistance. Force platform data were used to determine heel strike and toe off of stance phase. Ground reaction force and joint kinematic and kinetic data were reported elsewhere.4 EMG signals were rectified first and then smoothed using a root mean squared method with a 20-ms moving window. For each muscle, onset of muscle activation was defined as a rise in the EMG signal amplitude greater than the baseline plus two standard deviations during quiet standing, lasting longer than 50 ms. Offset of muscle activation was defined as the decrease in EMG signal amplitude below the baseline plus two standard deviations lasting longer than 50 ms. Onsets were temporally normalized to the duration of the stance phase starting from heel strike (Eq. (1)). Therefore, the onset of muscle activation prior to heel strike is represented as a negative percent. Duration of muscle activity was calculated as the difference between onset and offset of muscle activity and was normalized to the duration of the stance phase (Eq. (2)). M. TA activation onsets and durations were calculated for the load response (TA-LR) and pre-swing (TA-PS) portions of the stance phase.

The picture is emerging that the regulation of tax and HBZ

expression from the provirus plays a central role in the persistence and pathogenesis of HTLV-1 infection [20]. To summarize: since both tax and HBZ gene products promote proliferation of the infected cell, both have been suggested as necessary and sufficient Volasertib order causes of both the oligoclonal T cell proliferation seen in HTLV-1 infection and the pathogenesis of inflammatory and malignant diseases associated with HTLV-1. The potential pathogenic role of these viral gene products must be understood in the context of their normal physiological function in the life history of HTLV-1, since the primary function of these viral genes is not to cause disease in the host but rather to promote survival and propagation of the virus. The central question therefore becomes this: what regulates the expression of the tax and HBZ genes in vivo, and so controls the number, abundance and pathogenicity of HTLV-1-infected T cell clones in vivo? To answer this question, we must consider what differs between two clones of T cells naturally infected with HTLV-1. There are three principal attributes that distinguish one infected T cell clone from another: antigen (TCR) specificity, epigenetic modifications, and the genomic site of integration of the HTLV-1 provirus. In addition, as a consequence of

Selleck NLG919 the epigenetic modifications, there may be differences among clones in the expression of certain cell surface markers. We have hypothesized that the chief factor that regulates the expression of the HTLV-1 provirus is the integration site of the provirus in the host genome. To test this hypothesis, we recently developed [72] a sensitive, high-throughput technique for the mapping and – crucially – quantification of HTLV-1-infected T cell clones in fresh uncultured peripheral

blood mononuclear cells (PBMCs). We have used this protocol to address the following questions: • How many proviruses are present in each cell? The high-throughput integration site protocol [72] Pertussis toxin consists of PCR amplification of genomic DNA fragments to which a partially double-stranded DNA linker has been ligated. The protocol differs in a critical respect from preceding high-throughput retroviral mapping techniques. Instead of using restriction enzymes to digest the genomic DNA before linker ligation, the DNA is fragmented by sonication. The resulting quasi-random distribution of DNA fragment lengths confers two crucial advantages. First, it abrogates the biased detection – due to preferential amplification of short fragments – of proviruses integrated close to a given restriction enzyme site. Second, since the DNA shear sites are virtually random, each sister cell of a given HTLV-1-infected T-cell clone can be identified by the unique length of the amplicon.

Secondary antibodies were applied in blocking buffer for 30 min at room temperature, nuclei were stained for 3 min with DAPI and samples were mounted in Aqua Polymount this website (Polysciences). The CA1 region of hippocampal slices was imaged using a Zeiss LSM780 confocal microscope and a 40× oil objective (plan achromate, NA 1.4). Z-stacks spanning the entire thickness of the slice were obtained and channels were separated and collapsed to a

maximum intensity projection in ImageJ. For representation purposes, the channels corresponding to the detected mRNA and the DAPI staining were converted to binary images with fixed thresholds within an experiment for control and experimental sections. The mRNA puncta were dilated three times for better visualization. Both processed channels were buy Stem Cell Compound Library merged using Adobe Photoshop. Using the in situ hybridization data, each investigated dendrite was divided in bins of 25 μm and signal puncta were counted per bin. A master dendrite was made for every transcript with the average

number of puncta per bin assigned to the bin. We used sum norm to normalize the row expression vector for each candidate to make transcripts comparable, normalizing for differences in total expression levels. A hierarchical clustering algorithm was used to group the normalized expression vectors of all transcripts. As a dissimilarity measure, we used 1 minus the standardized covariance of the signal and the linkage option was the average

of the dissimilarities. We visualized the resulting dendrogram in MATLAB. Four main clusters were identified by the above procedure. In order to measure how faithfully the dendrogram preserves the pairwise distances between the original unmodeled data points, we calculated the cophenetic correlation coefficient. We also addressed the significance of the generation of the four main clusters as previously described (Varshavsky et al., 2008). Financial support was provided in part by the DFG-funded Collaborative Research Center 902: “Molecular Principles of RNA-based Regulation.” We are extremely grateful to Mona Khan, Christian Lozanoski, and Peter Mombaerts for assistance with the Nanostring technology. We thank Ben Barres for discussions trans-isomer order on glial transcriptomes. We thank Ed Lein for assistance in compiling interneuron-enriched transcripts. We thank Ina Bartnik for the preparation of cultured hippocampal neurons. We thank Gilles Laurent, Mona Khan, and Schuman lab members for comments on the manuscript. “
“In studies using functional magnetic resonance imaging (fMRI), elevated hippocampal activation is observed in a number of conditions that confer risk for Alzheimer’s disease (AD), including cognitively normal carriers of the ApoE4 allele ( Bookheimer et al., 2000, Trivedi et al., 2008, Filippini et al.

57, p < 0.05), and P7 (fold change = 2.86, p < 0.01) IP-astrocyte inserts (Figures 5G and 5H). Thus, IP-astrocytes are as capable of inducing structural synapses in RGC cultures as MD astrocytes are. Structural synapses are not indicative of functional synapses, thus we analyzed synaptic activity of the RGCs in the presence of a feeder layer of astrocytes. Previous studies have shown that the number of functional synapses increases significantly with an MD-astrocyte feeder layer (Ullian et al., 2001). selleck inhibitor We found that

both the frequency and amplitude of miniature excitatory postsynaptic currents (mEPSCs) increased significantly and to a comparable degree with feeder layers of IP-astrocytes P1 or P7, to that observed with an MD-astrocyte feeder layer (Figures 5I–5L). Taken together, these results show that IP-astrocytes buy ZD1839 retain functional properties characteristic of astrocytes. Intracellular calcium oscillations have been observed in astrocytes in vivo and are considered an important functional property of astrocytes and may aid in regulation of

blood flow or neural activity (Nimmerjahn et al., 2009). Several stimuli have been implicated in initiating calcium waves in MD-astrocytes. We used calcium imaging with Fluo-4 to investigate if IP-astrocytes exhibit calcium rises in response to glutamate, adenosine, potassium chloride (KCl), and ATP and if the nature of their response was similar to MD astrocytes (Cornell-Bell et al., 1990, Jensen and Chiu, 1991, Kimelberg et al., 1997 and Pilitsis and Kimelberg, 1998). Few calcium oscillations were observed at rest in IP-astrocytes, contrary to MD-astrocytes. A single cell in confluent cultures of P7 IP-astrocytes would respond independently of its neighbors. Such isolated and spontaneous firing of astrocytes has previously been observed in brain slices (Nett et al., 2002 and Parri and Crunelli, 2003).

In contrast, rhythmic calcium activity and regular spontaneous activity were observed in MD-astrocytes grown in the same media as cultured IP-astrocytes P7 (Figures 6A and 6C). Both MD-astrocytes and IP-astrocytes responded to 10 μM of adenosine (100% of MD-astrocytes, 89.6% ± 5.5% of IP-astrocytes; Figures S2C and S2D), 50 μM of glutamate (100% of MD-astrocytes, 88.1% ± 7.9% of IP-astrocytes; Figures S2E and SDHB S2F), and 100 μM of ATP (94.4% ± 5.5% of MD-astrocytes, 92.5% ± 1.5% of IP-astrocytes; Figures 6A and 6B) with increased frequency of calcium oscillations and/or amplitude of calcium oscillations. Both have several P2X and P2Y receptors and adora1 and adora2b receptors and thus can respond to these stimuli. Both MD and IP-astrocytes express mRNA for ionotropic glutamate receptors, but only the latter have metabotropic receptors (accession record number, GSE26066). Thus, the second phase calcium response observed with glutamate in IP-astrocytes after a period of quiescence, could be a metabotropic response. This was not observed in MD-astrocytes.

, 1998, Kowalski et al., 1996 and Schnupp et al., 2001). Figure 5A shows the excitatory Selleckchem AZD5363 and inhibitory synaptic receptive fields of a DS neuron selective to upward sweeps (DSI: 0.56). The size of the excitatory synaptic receptive field of this neuron was much smaller than that of the inhibitory synaptic receptive field (Figures 5A and 5B). This was a prominent characteristic of DS neurons that we encountered in the IC (Figure 5B; Figure S5 shows the raw traces of neurons presented

here). The bandwidths of the inhibitory inputs were much wider than that of the excitatory inputs for DS neurons (Figure 5C). Our data indicate that the receptive fields of the excitatory inputs were not balanced or overlapped with that of the inhibitory inputs, which differs from cortical DS neurons (Wehr and Zador, 2003 and Zhang et al., 2003). However, the inhibitory inputs to pure tones were always delayed to the excitatory see more inputs by 1–3 ms across the tested frequency domain, which suggests feedforward disynaptic connections of inhibitory neurons to the recorded neurons (Figure 5D) (Wehr and Zador, 2003 and Zhang et al., 2003). The flat distribution of the onset latencies of the synaptic inputs evoked

by tone pips rules out the existence of systematically delayed synaptic inputs crossing the frequency domain (Figure 5D). We also observed spectral asymmetry of synaptic receptive fields (Figures 5A–5C). For the low CF neurons with upward selectivity, the excitatory and inhibitory inputs overlapped at low frequencies, but the inhibitory inputs extended beyond the excitatory

synaptic receptive fields into high frequencies. For the high CF neurons with downward selectivity, the excitatory and inhibitory inputs overlapped at high frequencies, but the inhibitory inputs extended beyond the excitatory synaptic receptive field into low frequencies. For the middle CF neurons showing weak direction selectivity, their synaptic receptive fields of excitatory and inhibitory inputs were overlapped and covaried. Our results suggest that such configurations of excitatory and inhibitory input receptive fields might be the synaptic substrate underlying the topography of direction selectivity observed in higher auditory nuclei, Non-receptor tyrosine kinase e.g., primary auditory cortex (Zhang et al., 2003). To understand how the temporal asymmetry is generated as in Figure 4, we tested whether the onset and the duration of each response evoked by FM sweeps were reflected by the timing of the sweep’s intersection with the TRFs of the synaptic responses. We compared the timing of the FM-evoked synaptic responses and the calculated timing of responses when the frequency component of FM sweep putatively reached to the boundaries of TRFs. The highly correlated relationship suggests that the temporal imbalance of excitation and inhibition evoked by opposing directions of FM sweeps can be attributed to the asymmetric extension of inhibitory synaptic input receptive fields (Figure 5E).

Of note, we detected no difference in the number of PV+ interneurons in the hippocampus (data not shown). At P24, the mutant had an ∼15% reduction in the number of striatal PV+ interneurons; no statistically significant difference was observed in the number of striatal interneurons expressing CR, NPY, or SOM (Table S3). We demonstrated that the Lhx6PLAP/PLAP;Lhx8−/−

mutant fails to express Shh in early-born MGE MZ neurons ( Figure 1). To investigate whether LHX6 and/or LHX8 can directly regulate Shh expression we utilized a 384 bp enhancer element Ferroptosis cancer (SBE3) from the Shh locus that drives expression in the MGE MZ at E10.5 ( Figure 8A; Jeong et al., 2006). Using a bioinformatic approach (see Supplemental Experimental Procedures), we identified one putative LHX binding site in the SBE3 enhancer (site A; Figure 8A). Electrophoretic mobility shift assay (EMSA) showed that both LHX6 and LHX8 bind to SBE3; binding was greatly reduced when LHX site A was mutated ( Figures Venetoclax 8B and 8C). Next, we cloned SBE3 upstream of a minimal promoter and the mCherry coding sequence. We tested whether Lhx6 and/or Lhx8 promoted reporter gene expression in primary cultures from E12.5 MGE. mCherry+ cells were detected by immunofluorescence.

Lhx6, Lhx8, and Lhx6&8 increased mCherry expression roughly 3- to 4-fold (n = 4, p < 0.05; Figure 8D). On the other hand, when wild-type SBE3 was replaced with mutant SBE3 (site A), there was a ∼2.5-fold reduction in activation by Lhx6 and Lhx6&8 (p < 0.05); to there was a similar trend for Lhx8 reduction, but it was not statistically significant (n = 4; Figure 8D). Therefore, these results provide evidence that Lhx6 and Lhx8 can activate transcription in part through the LHX-binding site A in SBE3. Lhx6 and Lhx8 each has prominent individual functions in regulating the development of GABAergic and cholinergic neurons generated in the MGE ( Zhao et al., 2003, Zhao et al., 2008, Mori et al., 2004, Alifragis et al., 2004, Fragkouli et al., 2005, Fragkouli et al., 2009 and Liodis et al., 2007). Here, by analyzing mice lacking both genes we demonstrated that Lhx6 and Lhx8

also have redundant functions. A key early redundant function is to promote Shh expression in neurons of the MGE mantle zone ( Figure 1); SHH production by these cells then regulates the properties of the overlying SHH-negative MGE progenitor zone, including the expression of Gli1, Nkx6-2 and Ptc1 ( Figure 8E). Later in this discussion, we will expand upon the function of Shh expression in the MGE neurons. Lhx6 and Lhx8 together regulate the molecular properties of the MGE SVZ; the double mutant showed reduced expression of the Lmo3 and Nkx2-1 transcription factors that was greater than in the single mutants ( Figures 2 and S2). Nkx2-1 is essential in the VZ to specify MGE identity ( Sussel et al., 1999, Flandin et al., 2010 and Butt et al.

Unfortunately, the proposed “proteinopathy” cascade of events cannot explain a number of important factors critical for understanding neurodegeneration. For example, mutant proteins are ubiquitously expressed by neurons (and typically nonneuronal TSA HDAC manufacturer cells in the CNS), yet for each disorder, neurodegeneration occurs in selectively vulnerable cell populations. If a common molecular cascade can explain pathogenesis, why then are certain types of neurons more

vulnerable than others? Furthermore, most neurodegenerative disease, associated with protein misfolding, develops in middle or late adulthood, but the responsible proteins are expressed throughout the patient’s lifetime. How does age or time influence the pathogenic potential of a mutant or misfolded protein that characterizes a specific disease? Some speculate that the proteinopathy cascade may manifest in vivo only during the final stages of the degenerative selleck process. Thus, the anatomic, functional, or

age-dependent features that drive the proteinopathy cascade in subsets of neurons at a specific time remain undefined. One hypothesis potentially explaining how neurodegenerative diseases are initiated in their characteristic patterns was adopted from the study of cancer. The “multi-hit” theory of carcinogenesis addresses a number of key features of this disease, including the increased incidence of cancer with age and the clear influence of both genetic background and environmental exposures. That neurodegenerative disorders are similarly initiated by a combination of acquired and inherited cellular/molecular abnormalities has been proposed to explain the epidemiology of sporadic disease (Mahley et al., 2007 and Sulzer, 2007). We hypothesize that a multi-hit paradigm involving the impact of synergistic forms of cellular dysfunction via cell-cell interaction may account for both age dependence and regional specificity of neurodegeneration for a specific disorder. A corollary to this hypothesis is that disease-causing mutations result in cell type specific dysfunctions, which individually do

not cause the full spectrum of disease symptoms, but in concert and over time will result in the distinct patterns of neurological dysfunction Adenosine triphosphate and/or neurodegeneration that characterize a given disorder. Support for this hypothesis is found in numerous studies suggesting that disease pathogenesis in neurodegenerative syndromes involves communication between different cell types. Interacting cell types in different diseases are one unit of organization, defined by certain populations of neurons, surrounding glia, elements of the neurovascular interface, and CNS innate immune system. This hypothesis is consistent with recent, intriguing evidence for the prion-like spread of pathogenic misfolded proteins from cell to cell (Aguzzi and Rajendran, 2009).

All putative de novo CNVs detected in our whole-genome scans were independently validated on second custom

tiling array platform. A custom Agilent 1M array was designed with dense coverage (average probe spacing of 200 bp) of all putative de novo CNV regions. Samples were coded and hybridizations were done in random order to avoid any plate effects. Two-color hybridizations were performed with two micrograms of sample and reference DNA (CHP-SKN-1) and hybridized Nintedanib cost to the array at the Oxford Gene Technology service laboratory (Cambridgeshire, UK). Raw intensity data were normalized by Oxford Gene Technology service lab using Agilent’s recommended normalization method. Experiments with poor derivative log2 ratio spread (DLRS > 0.2) were repeated. We

received normalized intensity data on all samples from Oxford Gene technology in one batch. Probe Log2 Ratios were then standardized within each array. Detection of rare CNVs was performed using MeZOD as follows. For each CNV region that was defined in our whole-genome scans, we computed the median Z score of tiling array probes in each individual. The median of a region was then standardized vertically across all individuals. We then assign deletion genotypes using a Z score threshold of ≤ −2 and duplication genotypes using a Z score threshold of ≥ +2. Positive CNV calls were further verified by manual inspection of log2 ratios in the subject, mother, and father. Representative examples of validated de Ipatasertib concentration novo deletions are shown in Figure 1 and Figure 2.

The details of the number of putative de novo CNVs identified in BD, SCZ, and controls and their validation by tiling array CGH are described in Table S3. The rates of validations are presented in Table S4. The overall validation rate of putative de novo CNVs was 16% (23/145). As expected, the validation rate was highest for CNVs > 100 kb in size and lowest (3%) for CNVs that were < 20 kb in size. We evaluated the performance of our de novo CNV calling method by: i) analyzing a small set of 45 ASD trios included in our previous CNV study (Sebat et al., 2007) and, ii) by comparing results on validated control de novo CNVs identified by our group with results from a recent Dextrose study(Levy et al., 2011) by Mike Wigler’s group. In 45 ASD trios we detected and validated all 3 de novo CNVs that were identified in our previous study and in addition, we identified one novel de novo CNV 38 kb in size (Table S8). We compared our list of validated control de novo CNVs with de novo CNVs reported by (Levy et al., 2011) in the same 426 control trios using an entirely different informatics approach to identify de novo CNVs. Both groups identified four validated de novo CNVs in controls and therefore observed an identical rate (0.9%) of de novo CNVs in 426 controls. Three out of four de novo events overlapped between two groups. One de novo event that was unique to each group was < 20 kb in size.

, 2011). Dozens of miRNA were significantly up- or downregulated at each time point; however, the overlap between the initial response at 1 hr and the long-term response at 24 hr was less than 25% (Figure 2E). When cultured hippocampal cells were profiled after pharmacological stimulation in vitro to compare to miRNA changes after fear conditioning, just over half of those with detectable changes were found in both the in vitro and in vivo models (Figure 2F). This suggests that while cell culture models for neuronal plasticity can serve as very convenient systems to manipulate miRNA

that also provide impressive access to neuronal cell biology, analysis using in vivo models is essential. Interestingly, when downstream target gene mRNAs altered in both in vitro and in vivo were compared (Kye et al., 2011), several components in the miRNA core biosynthetic pathway were found to be part of the adaptive response (including DGCR8, Drosha, and Dicer), check details consistent with other studies suggesting that miRNA processing is actively coupled to neuronal activity in order to propel synaptic plasticity (see below). The components of the miRNA biogenesis and processing machinery are well conserved across the animal kingdom. After transcription, pri-miRNA is processed by RNase III domain-containing protein Drosha

in association with the RNA binding protein encoded by DIGeorge syndrome critical region gene 8 (DGCR8)/Pasha (reviewed by Du and Zamore, 2005). This “microprocessor” complex binds to the lower Gefitinib research buy stem region of the miRNA self-complementary region (Carthew and Sontheimer, 2009). The double-stranded stem and flanking regions are both important for DGCR8 binding and subsequent Drosha cleavage (Zeng and Cullen, 2006; Han et al., 2006; reviewed by Kim et al., 2009). Processed miRNA precursors

Adenosine (pre-miRNA) are then exported from the nucleus and cleaved by the RNase III domain-containing protein Dicer. Finally, the remaining duplex is loaded on to the RISC, which is comprised of a set of proteins that mediate mRNA target recognition and suppression, including Ago1, Ago2, Pumilio2 (Pum2), and Moloney leukemia virus (MOV10) (Du and Zamore, 2005). Pioneering studies of nervous system development using maternal-zygotic mutants of zebrafish dicer revealed gross morphological defects specifically in early brain patterning and morphogenesis ( Giraldez et al., 2005). Surprisingly, these dramatic abnormalities are largely rescued by reintroduction of miR-430 family members, suggesting that the complexity of miRNA control over the early stages of neural development may be quite limited. However, detailed studies of later stages in neural development have begun to suggest a more extensive contribution of miRNAs in the formation of synaptic connections, circuit maturation, and the activity-driven plasticity of these connections. Part of this evidence came from knockout mutations of the miRNA processing genes.

16, p < .05), thus reinforcing our findings of low physiological arousal in those more prone to risky substance use. These findings are in line with earlier suggestions that physiological stress response dysregulation in adolescents may signal vulnerability to various kinds of psychopathology ( Stroud et al., 2009). This is the first study to examine the relation between alcohol use and HR in a general adolescent population, therefore, the results are preliminary and must be interpreted cautiously. Our finding that those who drank more portrayed a lower HR during the stress procedure is in line with one finding in adults with a FH of alcoholism (Sorocco et al., 2006), though in contrast

to other similar studies which found increased Selleckchem RG7420 HR in response to unavoidable shock (Finn et al., 1992 and Finn and Pihl, 1987) and a mental arithmetic task (Harden and Pihl, 1995). Further research in this area is needed in order to clarify these contrasting findings. We observed that PS was significantly

and positively related to HR, which confirmed findings from a previous study in adolescents from the general population (Oldehinkel et al., 2011). We did not find a relation between PS and alcohol and tobacco use, corroborating earlier reports of no difference in PS between control subjects and those at risk for a SUD (Finn and EPZ5676 Pihl, 1987) see more and those exhibiting more externalizing problems (Fairchild et al., 2008). This was in line with our expectations; physiological responses reflect underlying, biological processes, and we would not necessarily expect similar relations to be found with the subjective experience

of a stressor. Physiological and perceived stress are distinct constructs (Oldehinkel et al., 2011), which was substantiated in our finding of a significant and positive, but not strong, correlation between HR and PS. Our observations indicate a relation between tobacco use and HR reactivity. Those who smoked every day showed a blunted HR response to the stressful tasks compared to those who smoked less frequently or not at all. This finding is in line with several findings on adult smokers (Girdler et al., 1997, Phillips et al., 2009, Roy et al., 1994, Sheffield et al., 1997 and Straneva et al., 2000) though is in contrast to other studies (Back et al., 2008, Childs and de Wit, 2009, Hughes and Higgins, 2010, Kirschbaum et al., 1993, Perkins et al., 1992 and Tersman et al., 1991). While two studies examining HR reactivity in low versus high frequency tobacco users found no difference between these groups (both portrayed attenuated responses), we found that adolescents who smoked less frequently did not differ significantly from those who had never smoked. It is possible that in adolescents, underlying variation of the ANS is only evident in those who use tobacco more frequently.