Faculty Bibliography

RATIONALE: We recently introduced a new rat model of emotional hyperthermia in which a salient stimulus activates brown adipose tissue (BAT) thermogenesis and tail artery constriction. Antipsychotic drugs, both classical and second generation, act to reduce excessive assignment of salience to objects and events in the external environment. The close association between salient occurrences and increases in body temperature suggests that antipsychotic drugs may also reduce emotional hyperthermia. OBJECTIVES: We determined whether chlorpromazine, clozapine, and risperidone dose dependently reduce emotionally elicited increases in BAT thermogenesis, cutaneous vasoconstriction, and body temperature in rats. METHODS: Rats, chronically instrumented for measurement of BAT and body temperature and tail artery blood flow, singly housed, were confronted with an intruder rat (confined within a small wire-mesh cage) after systemic pre-treatment of the resident rat with vehicle or antipsychotic agent. BAT and body temperatures, tail blood flow, and behavioral activity were continuously measured. RESULTS: Clozapine (30 mug-2 mg/kg), chlorpromazine (0.1-5 mg/kg), and risperidone (6.25 mug-1 mg/kg) robustly and dose-relatedly reduced intruder-elicited BAT thermogenesis and tail artery vasoconstriction, with consequent dose-related reduction in emotional hyperthermia. CONCLUSIONS: Chlorpromazine, a first-generation antipsychotic, as well as clozapine and risperidone, second-generation agents, dose-dependently reduce emotional hyperthermia. Dopamine D2 receptor antagonist properties of chlorpromazine do not contribute to thermoregulatory effects. Interactions with monoamine receptors are important, and these monoamine receptor interactions may also contribute to the therapeutic effects of all three antipsychotics. Thermoregulatory actions of putative antipsychotic agents may constitute a biological marker of their therapeutic properties.

Posttraumatic stress disorder (PTSD) is associated with increased risk for Type 2 diabetes and cardiovascular disease (cardiometabolic disease), warranting research into targeted prevention strategies. In the present case-control study of 160 young (mean age 32.7 years) male military veterans, we aimed to assess whether PTSD status predicted increased markers of cardiometabolic risk in otherwise healthy individuals, and further, to explore biological pathways between PTSD and these increased markers of cardiometabolic risk. Toward these aims, we compared measures of cardiometabolic risk, namely insulin resistance (IR) (HOMA-IR), metabolic syndrome (MetS) and prediabetes, between 80 PTSD cases and 80 controls without PTSD. We then determined whether PTSD-associated increases in HOMA-IR were correlated with select biological variables from pathways previously hypothesized to link PTSD with cardiometabolic risk, including systemic inflammation (increased C-reactive protein, interleukin-6, and tumor necrosis factor alpha), sympathetic over-activity (increased resting heart rate), and neuroendocrine dysregulation (increased plasma cortisol or serum brain-derived neurotrophic factor (BDNF)). We found PTSD diagnosis was associated with substantially higher HOMA-IR (cases 4.3+/-4.3 vs controls 2.4+/-2.0; p<0.001), and a higher frequency of MetS (cases 21.3% vs controls 2.5%; p<0.001), but not prediabetes (cases 20.0% vs controls 18.8%; p>0.05). Cases also had increased pro-inflammatory cytokines (p<0.01), heart rate (p<0.001), and BDNF (p<0.001), which together predicted increased HOMA-IR (adjusted R2=0.68, p<0.001). Results show PTSD diagnosis in young male military veterans without cardiometabolic disease is associated with increased IR, predicted by biological alterations previously hypothesized to link PTSD to increased cardiometabolic risk. Findings support further research into early, targeted prevention of cardiometabolic disease in individuals with PTSD.

Posttraumatic stress disorder (PTSD) is common in the general population, yet there are limitations to the effectiveness, tolerability, and acceptability of available first-line interventions. We review the extant knowledge on the effects of marijuana and other cannabinoids on PTSD. Potential therapeutic effects of these agents may largely derive from actions on the endocannabinoid system and we review major animal and human findings in this area. Preclinical and clinical studies generally support the biological plausibility for cannabinoids' potential therapeutic effects, but underscore heterogeneity in outcomes depending on dose, chemotype, and individual variation. Treatment outcome studies of whole plant marijuana and related cannabinoids on PTSD are limited and not methodologically rigorous, precluding conclusions about their potential therapeutic effects. Reported benefits for nightmares and sleep (particularly with synthetic cannabinoid nabilone) substantiate larger controlled trials to determine effectiveness and tolerability. Of concern, marijuana use has been linked to adverse psychiatric outcomes, including conditions commonly comorbid with PTSD such as depression, anxiety, psychosis, and substance misuse. Available evidence is stronger for marijuana's harmful effects on the development of psychosis and substance misuse than for the development of depression and anxiety. Marijuana use is also associated with worse treatment outcomes in naturalistic studies, and with maladaptive coping styles that may maintain PTSD symptoms. Known risks of marijuana thus currently outweigh unknown benefits for PTSD. Although controlled research on marijuana and other cannabinoids' effects on PTSD remains limited, rapid shifts in the legal landscape may now enable such studies, potentially opening new avenues in PTSD treatment research.

Background: Entropy has a fundamental relationship with information and the functioning of all computational systems. Entropy is defined as the number of states available to a system. A system with low entropy has access to fewer states than does one with high entropy. A system with low entropy is more ordered and more predicable than a system with high entropy. Since entropy is related to the functioning of computational systems, there is an emerging theoretical and empirical literature about its role in brain function and dysfunction. We present the results of three integrated studies applying resting state fMRI entropy measurement to understand intelligence, personality, and psychopathology. Brain entropy is an index of an individual's access to brain states at a given time and is measured through the predictivity of brain state over time. Thus, we would expect to observe brain entropic differences between conditions known to be associated with high flexibility (e.g. high intelligence, creativity, novelty seeking) vs. conditions associated with high rigidity (e.g. anxiety, depression, Posttraumatic Stress). The three studies are: Brain entropy and intelligence in 926 adults from the Brain Genomic Superstruct Project, 2. Brain entropy and personality in 926 adults from the Brain Genomic Superstruct Project, and 3. Brain entropy and PTSD in 95 veterans from the NYU Cohen Veterans Data Set. Methods: Subjects: Study 1 (Entropy and Intelligence) and Study 2 (Entropy and Personality) were conducted with data from the Brain Genomics Superstruct Project (BGSP). The BGSP includes 1570 healthy adult participants between the ages of 18 and 35. The current study utilized data from the 926 participants who completed intelligence and personality assessments. Study 3 (Entropy and PTSD) was conducted with data from the NYU Cohen Veterans Data Set. This data set includes 95 combat veterans, 46 with PTSD and 49 without PTSD. fMRI Procedures: Brain Genomics Superstruct Project (BGSP). All MRI data were obtained with 3T Trio scanners (Siemens Healthcare, Erlangen, Germany) at Harvard University and Massachusetts General Hospital. MRI scans for each participant included a high resolution structural scan (T1-weighted multi-echo MPRAGE, TR = 2.2 sec, TE = 1.5/3.4/5.2/7.0 msec, slices = 144, resolution = 1.2 x 1.2 x 1.2 mm) and a resting-state functional scan sensitive to blood oxygenation level-dependent (BOLD) contrast (TR = 3.0 sec, TE = 30 msec, slices = 47, resolution = 3.0 x 3.0 x 3.0 mm, 120 measurements). NYU Cohen Veterans Data Set: All MRI data were obtained with a 3T Trio scanner (Siemens AG, Erlangen Germany). Anatomical images were acquired with magnetization prepared rapid gradient echo sequence with TE/TI/TR = 2.98/900/2300 ms, 256 x 240 matrix, 256 mm x 240 mm fieldof-view, flip angle = 9degree, slice thickness = 1 mm and total slice number = 191; resting state fMRI was obtained using an echo-planar imaging sequence (TR/TE = 2000/29 ms, flip angle = 90degree), 64 x 64 matrix, pixel size 3.125 mm x 3.125 mm, total slice number = 32, slice thickness = 3.5 mm (without gaps), total volume number = 200. fMRI Entropy Analysis: Brain entropy was calculated using the Brain Entropy Mapping Toolbox (BENtbx) (Wang et al, 2014) for MATLAB (MATLAB Release R2015b, The MathWorks Inc., Natick, MA, United States). The BENtbx utilizes Sample Entropy (SampEn). For a given time series, SampEn is a single number representing the predictability of the series. The entropy of highly predictable series is small, close to 0, indicating a lack of variation or disorder. The entropy of unpredictable series is large, indicating a high amount of variation or disorder. The Sample Entropy process first breaks a series into smaller sets of size m. For example, for m = 2, and the BOLD time series is broken into pairs of consecutive values. Each pair is then compared with every other pair to find the maximum distance (absolute value difference) between any number in the first pair and any number in the second pair. If the distance is less than the threshold r, the two pairs are considered a 'match.' This process is then repeated for sets of size m + 1. Sample Entropy is then the ratio: SampEn =-log A/B: Where, A = number of matches using sets of size m+1 and B = number of matches using sets of size m. For perfectly predictable series, A and B will be equal, and entropy will be 0. As disorder in a series increases, B will become greater than A, and the equation will yield an increasingly large positive number. Psychometric Measurement: Study 1: Intelligence was measured with the Shipley Estimated IQ, Vocabulary, and Matrix Reasoning scales. Study 2: Personality was measured for Behavioral Inhibition, Harm Avoidance, Risk Taking, and Novelty Seeking. Study 3: PTSD was measured with the Clinician Administered PTSD Scale (CAPS). Results: Study 1: Shipley Estimated IQ, Vocabulary, and Matrix Reasoning were all associated with higher brain entropy. In particular, Vocabulary was related to higher entropy in the L fusiform gyrus, inferior temporal gyrus, parahippocampal gyrus. Matrix Reasoning was associated with higher entropy in the bilateral superior, medial, inferior frontal gyrus, bilateral orbital gyrus, and R middle frontal gyrus. Study 2: Harm avoidance and Behavioral Inhibition were associated with lower entropy and Novelty Seeking and Risk Taking were associated with higher entropy. Study 3: PTSD was associated with lower entropy, particularly in the L hippocampus and parahippocampal gyrus, inferior and middle temporal lobes: and higher entropy in the R precuneus, and R parietal lobe. Conclusions: Brain entropy may provide a novel approach to understand intelligence, personality, and psychopathology such as PTSD

Independent component analysis (ICA) is a widely used technique for investigating functional connectivity (fc) in functional magnetic resonance imaging data. Masked independent component analysis (mICA), that is, ICA restricted to a defined region of interest, has been shown to detect local fc networks in particular brain regions, including the cerebellum, brainstem, posterior cingulate cortex, operculo-insular cortex, hippocampus, and spinal cord. Here, we present the mICA toolbox, an open-source GUI toolbox based on FSL command line tools that performs mICA and related analyses in an integrated way. Functions include automated mask generation from atlases, essential preprocessing, mICA-based parcellation, back-reconstruction of whole-brain fc networks from local ones, and reproducibility analysis. Automated slice-wise calculation and cropping are additional functions that reduce computational time and memory requirements for large analyses. To validate our toolbox, we tested these different functions on the cerebellum, hippocampus, and brainstem, using resting-state and task-based data from the Human Connectome Project. In the cerebellum, mICA detected six local networks together with their whole-brain counterparts, closely replicating previous results. MICA-based parcellation of the hippocampus showed a longitudinally discrete configuration with greater heterogeneity in the anterior hippocampus, consistent with animal and human literature. Finally, brainstem mICA detected motor and sensory nuclei involved in the motor task of tongue movement, thereby replicating and extending earlier results. Hum Brain Mapp, 2016. (c) 2016 Wiley Periodicals, Inc.

The hippocampus (HPC) is functionally heterogeneous along the longitudinal anterior-posterior axis. In rodent models, gene expression maps define at least three discrete longitudinal subregions, which also differ in function, and in anatomical connectivity with the rest of the brain. In humans, equivalent HPC subregions are less well defined, resulting in a lack of consensus in neuroimaging approaches that limits translational study. This study determined whether a data-driven analysis, namely independent component analysis (ICA), could reproducibly define human HPC subregions, and map their respective intrinsic functional connectivity (iFC) with the rest of the brain. Specifically, we performed ICA of resting-state fMRI activity spatially restricted within the HPC, to determine the configuration and reproducibility of functional HPC components. Using dual regression, we then performed multivariate analysis of iFC between resulting HPC components and the whole brain, including detailed connectivity with the hypothalamus, a functionally important connection not yet characterized in human. We found hippocampal ICA resulted in highly reproducible longitudinally discrete components, with greater functional heterogeneity in the anterior HPC, consistent with animal models. Anterior hippocampal components shared iFC with the amygdala, nucleus accumbens, medial prefrontal cortex, posterior cingulate cortex, midline thalamus, and periventricular hypothalamus, whereas posterior hippocampal components shared iFC with the anterior cingulate cortex, retrosplenial cortex, and mammillary bodies. We show that spatially masked hippocampal ICA with dual regression reproducibly identifies functional subregions in the human HPC, and maps their respective brain intrinsic connectivity. Hum Brain Mapp 37:462-476, 2016. (c) 2015 Wiley Periodicals, Inc.

Cannabidiol (CBD), a Cannabis sativa constituent, is a pharmacologically broad-spectrum drug that in recent years has drawn increasing interest as a treatment for a range of neuropsychiatric disorders. The purpose of the current review is to determine CBD's potential as a treatment for anxiety-related disorders, by assessing evidence from preclinical, human experimental, clinical, and epidemiological studies. We found that existing preclinical evidence strongly supports CBD as a treatment for generalized anxiety disorder, panic disorder, social anxiety disorder, obsessive-compulsive disorder, and post-traumatic stress disorder when administered acutely; however, few studies have investigated chronic CBD dosing. Likewise, evidence from human studies supports an anxiolytic role of CBD, but is currently limited to acute dosing, also with few studies in clinical populations. Overall, current evidence indicates CBD has considerable potential as a treatment for multiple anxiety disorders, with need for further study of chronic and therapeutic effects in relevant clinical populations.

OBJECTIVE: Clinical studies suggest resting thermoregulatory cutaneous vasomotor tone could be increased in schizophrenia, resulting in reduced hand blood flow. In animal models, atypical antipsychotics including clozapine potently inhibit sympathetic neural outflow to the thermoregulatory cutaneous vascular beds. We have now determined whether antipsychotic medication administration is associated with an acute increase in hand blood flow in patients with schizophrenia and schizoaffective disorder, and whether this increase correlates with clinical status. METHOD: Hand temperature was measured with an infrared camera in 12 patients with chronic schizophrenia or schizoaffective disorder 30 min prior to, then 30 and 60 min following medication. Clinical status was assessed via the Brief Psychiatric Rating Scale (BPRS). Results were compared using regression and repeated measures analysis of variance. RESULTS: A robust and significant increase in hand temperature (p < 0.001) was observed following antipsychotic administration. The mean increase after 60 min was 4.1 +/- 2.4 degrees C. This increase was significantly associated with colder hand temperature prior to medication (p < 0.05; suggestive of increased resting vasoconstriction) and with more severe psychiatric symptoms (p < 0.05). CONCLUSIONS: Atypical antipsychotics were associated with increased hand blood flow, consistent with inhibition of thermoregulatory sympathetic outflow to the cutaneous vascular bed in patients with schizophrenia and schizoaffective disorder. This increase correlated with symptom severity. Hand temperature increase following antipsychotic medication may therefore be a simple and informative physiological marker of disease activity and potential response in patients with schizophreniform disorders. Given that antipsychotics also inhibit sympathetic outflow to brown adipose tissue, which normally converts energy to heat, future studies should examine whether antipsychotic-induced hand temperature increase is associated with antipsychotic-induced weight gain.

The red-green axis of colour vision evolved recently in primate evolutionary history. Signals serving red-green colour vision travel together with signals serving spatial vision, in the parvocellular (PC) division of the subcortical visual pathway. However, the question of whether receptive fields of PC pathway cells are specialized to transmit red-green colour signals remains unresolved. We addressed this question in single-cell recordings from the lateral geniculate nucleus of anaesthetized marmosets. Marmosets show a high proportion of dichromatic (red-green colour-blind) individuals, allowing spatial and colour tuning properties of PC cells to be directly compared in dichromatic and trichromatic visual systems. We measured spatial frequency tuning for sine gratings that provided selective stimulation of individual photoreceptor types. We found that in trichromatic marmosets, the foveal visual field representation is dominated by red-green colour-selective PC cells. Colour selectivity of PC cells is reduced at greater eccentricities, but cone inputs to centre and surround are biased to create more selectivity than predicted by a purely 'random wiring' model. Thus, one-to-one connections in the fovea are sufficient, but not necessary, to create colour-selective responses. The distribution of spatial tuning properties for achromatic stimuli shows almost complete overlap between PC cells recorded in dichromatic and trichromatic marmosets. These data indicate that transmission of red-green colour signals has been enabled by centre-surround receptive fields of PC cells, and has not altered the capacity of PC cells to serve high-acuity vision at high stimulus contrast.

We measured functional input from short-wavelength selective (S) cones to neurons in the dorsal lateral geniculate nucleus (LGN) and striate cortex (area V1) in anaesthetized marmosets. We found that most magnocellular (MC) and parvocellular (PC) cells receive very little (<5%) functional input from S cones, whereas blue-on cells of the koniocellular (KC) pathway receive dominant input from S cones. Cells dominated by S cone input were not encountered in V1, but V1 cells received more S cone input than PC or MC cells. This suggests that S cone inputs are distributed broadly among neurons in V1. No differences in strength of S cone inputs were seen on comparing dichromatic and trichromatic marmosets, suggesting that the addition of a medium-long wavelength selective cone-opponent ("red-green") channel to a dichromatic visual system does not detectably affect the chromatic properties of the S cone pathways.