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Background Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error–driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex. Methods We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72). Results Using accuracy, there was a main effect of group (F3,279 = 7.87, p Conclusions Both medicated and unmedicated patients showed overreliance on prediction error–driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.

Item does not contain fulltext Dopamine contributes to cognitive control through well-established effects in both the striatum and cortex. Although earlier work suggests that dopamine affects cognitive control capacity, more recent work suggests that striatal dopamine may also impact on cognitive motivation. We consider the emerging perspective that striatal dopamine boosts control by making people more sensitive to the benefits versus the costs of cognitive effort, and we discuss how this sensitivity shapes competition between controlled and prepotent actions. We propose that dopamine signaling in distinct cortico-striatal subregions mediates different types of cost-benefit tradeoffs, and also discuss mechanisms for the local control of dopamine release, enabling selectivity among cortico-striatal circuits. In so doing, we show how this cost-benefit mosaic can reconcile seemingly conflicting findings about the impact of dopamine signaling on cognitive control.

Background: Maladaptive approach-avoidance behavior has been implicated in the pathophysiology of major depressive disorder (MDD), but the neural basis of these abnormalities in decision making remains unclear. Capitalizing on recent preclinical findings, we adapted an approach-avoidance conflict task from nonhuman primate research for use in human functional magnetic resonance imaging (fMRI). Methods: Forty-two female participants, including 18 unmedicated individuals with current MDD (mean age 25.2 ± 5.1 years) and 24 psychiatrically healthy control subjects (mean age 26.3 ± 7.6 years) completed the adapted approach-avoidance task during fMRI. To probe potential mechanistic factors underlying the observed behavioral and fMRI findings and to inform interpretation of putative group differences, we examined electrophysiological data from 2 female Macaca mulatta monkeys performing the approach-avoidance conflict task mimicked in the fMRI study. Results: Findings demonstrated congruent neural correlates of approach-avoidance conflict and aversive responsiveness in the anterior cingulate cortex, including the pregenual cortex, of human subjects and macaques (humans: p

International audience; Dopaminergic medications used in the treatment of patients with Parkinson's disease are associated with motor and non-motor behavioural side-effects, such as dyskinesias and impulse control disorders also known as behavioural addictions. Levodopa-induced dyskinesias occur in up to 80% of patients with Parkinson's after a few years of chronic treatment. Impulse control disorders, including gambling disorder, binge eating disorder, compulsive sexual behaviour, and compulsive shopping occur in about 17% of patients with Parkinson's disease on dopamine agonists. These behaviours reflect the interactions of the dopaminergic medications with the individual's susceptibility, and the underlying neurobiology of Parkinson's disease. Parkinsonian rodent models show enhanced reinforcing effects of chronic dopaminergic medication, and a potential role for individual susceptibility. In patients with Parkinson's disease and impulse control disorders, impairments are observed across subtypes of decisional impulsivity, possibly reflecting uncertainty and the relative balance of rewards and losses. Impairments appear to be more specific to decisional than motor impulsivity, which might reflect differences in ventral and dorsal striatal engagement. Emerging evidence suggests impulse control disorder subtypes have dissociable correlates, which indicate that individual susceptibility predisposes towards the expression of different behavioural subtypes and neurobiological substrates. Therapeutic interventions to treat patients with Parkinson's disease and impulse control disorders have shown efficacy in randomised controlled trials. Large-scale studies are warranted to identify individual risk factors and novel therapeutic targets for these diseases. Mechanisms underlying impulse control disorders and dyskinesias could provide crucial insights into other behavioural symptoms in Parkinson's disease and addictions in the general population.

We propose that schizophrenia involves a combination of decreased phasic dopamine responses for relevant stimuli and increased spontaneous phasic dopamine release. Using insights from computational reinforcement-learning models and basic-science studies of the dopamine system, we show that each of these two disturbances contributes to a specific symptom domain and explains a large set of experimental findings associated with that domain. Reduced phasic responses for relevant stimuli help to explain negative symptoms and provide a unified explanation for the following experimental findings in schizophrenia, most of which have been shown to correlate with negative symptoms: reduced learning from rewards; blunted activation of the ventral striatum, midbrain, and other limbic regions for rewards and positive prediction errors; blunted activation of the ventral striatum during reward anticipation; blunted autonomic responding for relevant stimuli; blunted neural activation for aversive outcomes and aversive prediction errors; reduced willingness to expend effort for rewards; and psychomotor slowing. Increased spontaneous phasic dopamine release helps to explain positive symptoms and provides a unified explanation for the following experimental findings in schizophrenia, most of which have been shown to correlate with positive symptoms: aberrant learning for neutral cues (assessed with behavioral and autonomic responses), and aberrant, increased activation of the ventral striatum, midbrain, and other limbic regions for neutral cues, neutral outcomes, and neutral prediction errors. Taken together, then, these two disturbances explain many findings in schizophrenia. We review evidence supporting their co-occurrence and consider their differential implications for the treatment of positive and negative symptoms.

Often the world is structured such that distinct sensory contexts signify the same abstract rule set. Learning from feedback thus informs us not only about the value of stimulus-action associations but also about which rule set applies. Hierarchical clustering models suggest that learners discover structure in the environment, clustering distinct sensory events into a single latent rule set. Such structure enables a learner to transfer any newly acquired information to other contexts linked to the same rule set, and facilitates re-use of learned knowledge in novel contexts. Here, we show that humans exhibit this transfer, generalization and clustering during learning. Trial-by-trial model-based analysis of EEG signals revealed that subjects’ reward expectations incorporated this hierarchical structure; these structured neural signals were predictive of behavioral transfer and clustering. These results further our understanding of how humans learn and generalize flexibly by building abstract, behaviorally relevant representations of the complex, high-dimensional sensory environment.

Contemporary psychiatry faces major challenges. Its syndrome-based disease classifi cation is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science.

This is the second of two companion papers proposing priority problems for research on mental disorders. Whereas the first paper focuses on questions of nosology and diagnosis, this Personal View concerns pathogenesis and aetiology of psychiatric diseases. We hope that this (non-exhaustive and subjective) list of problems, nominated by scientists and clinicians from different fields and institutions, provides guidance and perspectives for choosing future directions in psychiatric science.

Objective To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD). Method A total of 21 high-functioning individuals with ASD aged 12 to 18 years, and 23 gender-, IQ-, and age-matched adolescents with typical development (TYP), completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). Participants were trained on overlapping pairs in a stimulus hierarchy of colored ovals where A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD, BE) in a "Big Game." Whole-brain univariate, region of interest, and functional connectivity analyses were used. Results During training, the TYP group exhibited increased recruitment of the prefrontal cortex (PFC), whereas the group with ASD showed greater functional connectivity between the PFC and the anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably; however, functional connectivity between these regions was positively associated with TI performance for only the group with ASD. During the Big Game, the TYP group showed greater recruitment of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the group with ASD. Conclusion During TI, TYP individuals recruited cognitive control–related brain regions implicated in mature problem solving/reasoning including the PFC, parietal cortex, and ACC, whereas the group with ASD showed functional connectivity of the hippocampus and the caudate that was associated with task performance. Failure to reliably engage cognitive control–related brain regions may produce less integrated flexible learning in individuals with ASD unless they are provided with task support that, in essence, provides them with cognitive control; however, this pattern may normalize with age.

The extent to which we learn from positive and negative outcomes of decisions is modulated by the neurotransmitter dopamine. Dopamine neurons burst fire in response to unexpected rewards and pause following negative outcomes. This dual signaling mechanism is hypothesized to drive both approach and avoidance behavior. Here we test a prediction deriving from a computational reinforcement learning model, in which approach is mediated via activation of the direct cortico-striatal pathway due to striatal D1 receptor stimulation, while avoidance occurs via disinhibition of indirect pathway striatal neurons secondary to a reduction of D2 receptor stimulation. Using positron emission tomography with two separate radioligands, we demonstrate that individual differences in human approach and avoidance learning are predicted by variability in striatal D1 and D2 receptor binding, respectively. Moreover, transient dopamine precursor depletion improved learning from negative outcomes. These findings support a bidirectional modulatory role for striatal dopamine in reward and avoidance learning via segregated D1 and D2 cortico-striatal pathways.

Recent advancements in cognitive neuroscience have afforded a description of neural responses in terms of latent algorithmic operations. However, the adoption of this approach to human scalp electroencephalography (EEG) has been more limited, despite the ability of this methodology to quantify canonical neuronal processes. Here, we provide evidence that theta band activities over the midfrontal cortex appear to reflect a common computation used for realizing the need for cognitive control. Moreover, by virtue of inherent properties of field oscillations, these theta band processes may be used to communicate this need and subsequently implement such control across disparate brain regions. Thus, frontal theta is a compelling candidate mechanism by which emergent processes, such as 'cognitive control', may be biophysically realized.

SummaryHumans exhibit a preference for options they have freely chosen over equally valued options they have not; however, the neural mechanism that drives this bias and its functional significance have yet to be identified. Here, we propose a model in which choice biases arise due to amplified positive reward prediction errors associated with free choice. Using a novel variant of a probabilistic learning task, we show that choice biases are selective to options that are predominantly associated with positive outcomes. A polymorphism in DARPP-32, a gene linked to dopaminergic striatal plasticity and individual differences in reinforcement learning, was found to predict the effect of choice as a function of value. We propose that these choice biases are the behavioral byproduct of a credit assignment mechanism responsible for ensuring the effective delivery of dopaminergic reinforcement learning signals broadcast to the striatum.

We present a characterization of Hilbert C * -modules as Banach modules with involution context, in the sense explained below.

Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance.

We investigate orthonormality-preserving, C ⁎ -conformal and conformal module mappings on full Hilbert C ⁎ -modules to obtain their general structure. Orthogonality-preserving bounded module maps T act as a multiplication by an element λ of the center of the multiplier algebra of the C ⁎ -algebra of coefficients combined with an isometric module operator as long as some polar decomposition conditions for the specific element λ are fulfilled inside that multiplier algebra. Generally, T always fulfills the equality 〈 T ( x ) , T ( y ) 〉 = | λ | 2 〈 x , y 〉 for any elements x, y of the Hilbert C ⁎ -module. At the contrary, C ⁎ -conformal and conformal bounded module maps are shown to be only the positive real multiples of isometric module operators.

Individuals differ in their tendencies to seek positive decision outcomes or to avoid negative ones. At the neurobiological level, our model suggests that phasic changes in dopamine support learning to reinforce good decisions via striatal D1 receptors, and to avoid maladaptive choices via striatal D2 receptors. Accordingly, in a previous study individual differences in positive and negative learning were strongly modulated by two genetic polymorphisms factors related to striatal D1 and D2 function, respectively. Nevertheless, whereas the role for dopamine in positive learning is relatively well accepted, that in learning to avoid negative outcomes is more controversial. Here we further explore D2-receptor-related genetic contributions to probabilistic avoidance in humans, in light of recent data showing that particular DRD2 polymorphisms are associated with functional modulation of receptor expression [Zhang Y, Bertolino A, Fazio L, Blasi G, Rampino A, Romano R, Lee M-LT, Xiao T, Papp A, Wang D, Sadée W (2007) Polymorphisms in human dopamine d2 receptor gene affect gene expression, splicing, and neuronal activity during working memory. Proc Natl Acad Sci U S A 104(51):20552-20557]. We find that a promoter polymorphism rs12364283 associated with transcription and D2 receptor density was strongly and selectively predictive of avoidance-based decisions. Two further polymorphisms (rs2283265 and rs1076560) associated with relatively reduced presynaptic relative to postsynaptic D2 receptor expression were predictive of relative impairments in negative compared to positive decisions. These previously undocumented effects of DRD2 polymorphisms were largely independent of those we reported previously for the C957T polymorphism (rs6277) associated with striatal D2 density. In contrast, effects of the commonly studied Taq1A polymorphism on reinforcement-based decisions were due to indirect association with C957T. Taken together these findings suggest multiple D2-dependent genetic mechanisms contributing to avoidance. We discuss these effects in the context of neurocomputational models of reinforcement leaning in the basal ganglia.

Item does not contain fulltext Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S (Ed) 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

The basal ganglia (BG) coordinate decision making processes by facilitating adaptive frontal motor commands while suppressing others. In previous work, neural network simulations accounted for response selection deficits associated with BG dopamine depletion in Parkinson's disease. Novel predictions from this model have been subsequently confirmed in Parkinson patients and in healthy participants under pharmacological challenge. Nevertheless, one clear limitation of that model is in its omission of the subthalamic nucleus (STN), a key BG structure that participates in both motor and cognitive processes. The present model incorporates the STN and shows that by modulating when a response is executed, the STN reduces premature responding and therefore has substantial effects on which response is ultimately selected, particularly when there are multiple competing responses. Increased cortical response conflict leads to dynamic adjustments in response thresholds via cortico-subthalamic-pallidal pathways. The model accurately captures the dynamics of activity in various BG areas during response selection. Simulated dopamine depletion results in emergent oscillatory activity in BG structures, which has been linked with Parkinson's tremor. Finally, the model accounts for the beneficial effects of STN lesions on these oscillations, but suggests that this benefit may come at the expense of impaired decision making.

The prefrontal cortex has long been thought to subserve both working memory and "executive" function, but the mechanistic basis of their integrated function has remained poorly understood, often amounting to a homunculus. This paper reviews the progress in our laboratory and others pursuing a long-term research agenda to deconstruct this homunculus by elucidating the precise computational and neural mechanisms underlying these phenomena. We outline six key functional demands underlying working memory, and then describe the current state of our computational model of the prefrontal cortex and associated systems in the basal ganglia (BG). The model, called PBWM (prefrontal cortex, basal ganglia working memory model), relies on actively maintained representations in the prefrontal cortex, which are dynamically updated/gated by the basal ganglia. It is capable of developing human-like performance largely on its own by taking advantage of powerful reinforcement learning mechanisms, based on the midbrain dopaminergic system and its activation via the basal ganglia and amygdala. These learning mechanisms enable the model to learn to control both itself and other brain areas in a strategic, task-appropriate manner. The model can learn challenging working memory tasks, and has been corroborated by several important empirical studies.

SummaryThe error-related negativity (ERN) is an electrophysiological marker thought to reflect changes in dopamine when participants make errors in cognitive tasks. Our computational model further predicts that larger ERNs should be associated with better learning to avoid maladaptive responses. Here we show that participants who avoided negative events had larger ERNs than those who were biased to learn more from positive outcomes. We also tested for effects of response conflict on ERN magnitude. While there was no overall effect of conflict, positive learners had larger ERNs when having to choose among two good options (win/win decisions) compared with two bad options (lose/lose decisions), whereas negative learners exhibited the opposite pattern. These results demonstrate that the ERN predicts the degree to which participants are biased to learn more from their mistakes than their correct choices and clarify the extent to which it indexes decision conflict.

It is widely believed that the prefrontal cortex contributes to adap-tive control over prepotent or habitual behaviors. One of the proposedmeans of implementing control involves directed “top-down” inhibi-tion of actions. While this descriptive model is helpful, much work re-mains to be done to explain the distinct neural mechanisms by whichsuch top-down control alters action selection. In a recent report, Alegreet al. (2013) provide evidence that the cortex and subthalamic nucleus(STN) of the basal ganglia interact to both enable and inhibit behaviorby exchanging information across different oscillatory frequency bands.The STN are small subcortical nuclei that lie between the brainstemand the pallidum. Long considered a part of the cortico-striatal indirectpathway, they have been implicated as a part of an inhibitory systemthat prevents motor gating, acting in antagonism to the facilitatory di-rect pathway (Alexander and Crutcher, 1990; Mink, 1996). However,morerecenthistological (Nambuet al.,2002,2012), functionalconnec-tivity (Aron et al., 2007),and computational (Frank,2006) descriptionsof thecortico-basalganglia systemsuggestthat theSTNparticipate in athird extra-striatal information processing stream: the “hyperdirect”pathway. In this role, motor and premotor cortex project directly tothe STN (bypassing the striatum altogether), which projects to basalganglia output in order to act a global brake on striatal output. Thishyperdirect system is thus a compelling candidate “hold-your horses”mechanism by which high-level premotor areas of frontal cortex (suchasthemidcingulatecortexorpre-supplementalmotorarea)couldrapid-ly delay motor output due to signals of con flict between competing re-sponse options, buying time so the best decision can be made ( Frank,2006). While functional MRI studies show STN activation during re-sponse inhibition (e.g.,Aron et al., 2007), a better understanding ofthe detailed neuronal dynamics within the STN is needed to under-standhow this systemprocesses information andhowit cango awryin Parkinson's disease.Given the pathology of akinesia and bradykinesia in Parkinsonism,it should be apparent that removal of this STN brake on motor outputcan provide increased motor fluidity to patients, such that relativelyless amount of direct pathway activity is needed. Moreover, dopa-mine depletion can cause the STN to function aberrantly, increasingoscillatory coupling between the STN and globus pallidus and leadingtomotortremor(Bergman et al., 1998; Levy et al., 2000; Raz et al.,2000). Ameliorative treatment can be provided by interfering withSTN function via lesions (Bergman et al., 1990)ordeepbrainstimu-lation, which alters neuronal output and provides tremendous symp-tom relief including abolishment o f tremor. However, this procedurehas some unfortunate side effects in vulnerable individuals, where itcan increase impulsivity and induce poor decision making ( Frank et al.,2007;Halbigetal.,2009).Clearly,athoroughunderstandingofthisneu-ral system will not only have important implications for understandingbasic mechanisms of action selection, but also for optimizing treatmentfor Parkinsonism and other motor disorders.In their recent report, Alegre et al. (2013) capitalize on the abilityto record from the STN after implantation of deep brain stimulationelectrodes in Parkinsonian patients. In order to assess adaptive inhib-itory control, patients performed a stop signal task where a ‘go’ cueindicates the need for action, unless it is followed (at varying delays)by a ‘stop’ cue that indicates a need to cancel the action. Recordingswere supplemented by simultaneous scalp EEG over motor cortex, andwere performed both on and off dopaminergic medication. Given thatprevious reports have suggested that dorsal (motor) and ventral (cogni-tiveandaffective)areasoftheSTNmaydifferentiallycontributetoactionselection (Greenhouse et al., 2011; Hershey et al., 2010; Weinbergeret al., 2006), the spatial speci ficity of these recordings were considered,including the hemisphericlaterality of any potentialeffects.The findingsrevealed that different action selection processes were not only spatiallydistinct, but were also represented in different frequency bands in theEEG. Field oscillations at different frequencies allow neurons to ‘tune in’to different networks in order to communicate different types of infor-mation. In the case of EEG, Alegre et al. (2013) revealed that the theta(~4–6Hz),beta(~12–25 Hz)andgamma(~55–75 Hz)bandseachcon-tributedtodistinctaspectsofactionselectioninthecortico-STNnetwork.Neuronal activities in the beta band are thought to reflect a typeof active inhibition (e.g. “preserving the status quo”: Engel and Fries,2010). A diminishment in beta power during action readiness is thusobservedinbothcortexandSTN,possiblyduetodopaminergicin fluence(JenkinsonandBrown,2011).Dopaminedepletioncontributestoexces-sive synchronization of beta activities in STN ( Marceglia et al., 2011;Weinbergeretal.,2006),aswellaswithinnetworksincludingtheexter-nalglobuspallidus(Malletetal.,2008a),andcortex(Eusebioetal.,2009;Malletetal.,2008b),suggestingthatitenhancesthefunctionalroleofac-tive inhibition to a maladaptive degree. Conversely, activity in the thetaand gamma bands are thought to reflect active information processing(Fries,2009;Womelsdorfetal.,2010),whichsometimesinteracttogeth-er to facilitate information transfer across long distances or timescales(Canolty et al., 2006). The spectral width of each of these frequenciesalso appears to relate to the specificity of information content. Theta

In this report the authors present a review of complex regional pain syndrome and two case reports of complex regional pain syndrome that were recalcitrant to the usual treatments. The first case presented is a middle-aged female who developed signs and symptoms of post-traumatic complex regional pain syndrome. The second case is a woman with a pre-existing history of complex regional pain syndrome whose condition worsened after surgery despite appropriate perioperative precautions. These cases are unique because in both cases an early diagnosis of complex regional pain syndrome was established, yet they were both resistant to the usual treatment protocols.

The present study describes the application of differential scanning calorimetry (DSC) to ascertain the crystalline state of a drug with a melting point of approximately 53 degrees C after dispersion on hydrophilic carriers by either simple mixing or by fusion. The carriers examined include polyethylene glycol 6000 and colloidal silicon dioxides. The most interesting of the systems investigated, in which the drug is gradually transformed from the crystalline to the amorphous state at room temperature, are physical mixtures of the drug and colloidal silicon dioxides. The crystalline transformation is manifested by the gradual decrease in the endothermic transition energy of the physical mixture with time. The crystalline transformation is characteristically biphasic with initially fast first-order kinetics, followed by a slow conversion process. The rate of transformation is dependent on the drug-silicon dioxide ratio, temperature, and certain physical properties of the silicon dioxides. An inverse relationship exists between transition energy and the in vitro dissolution rate of the drug in the physical mixtures with silicon dioxide. This suggests that DSC may provide a useful method for evaluating the effects of formulation variables upon dissolution rate.

The absorption spectra of non-hydrolyzed Mn(CN)63− species, in cyanide as well as acidic media, has been characterized. In addition to the charge-transfer bands at 43,500 and 21,800 cm−1, the band at 27,000 cm−1 has been assigned to the first spin-allowed transition of 3 T 1g → 3 E g . The shoulders at 29,000 and 34,500 cm−1 are assigned to the other spin-allowed transitions. The spin-forbidden transitions of 3T1g to 5 E g , 1 T 2g , 1 E g , and 1 A 1g are placed at 12,000, 12,500, 13,500, and 24,500 cm−1, respectively. The absorption spectrum of Mn(CN)5(OH)3−, in 10 per cent perchloric acid, has also been obtained. Its bands at 26,250 and 25,450 cm−1 are justified in terms of a splitting of the 3 T 1g → 3 E g transition, due to the reduction in the symmetry of the complex from Oh to C4v.