Cognitive impairment and therapeutic response in resistant depression.

OBJECTIVES: Therapeutic response in depression is a major challenge since more than one third of patients are not in remission after two attempts of antidepressant treatment and will present a treatment-resistant depression. In order to better adapt therapeutic strategies for treatment-resistant patients, predictive indicators and markers of therapeutic response still need to be identified. In parallel, patients with depression exhibit disturbances in cognitive functioning. This study aims to describe and compare cognitive performances collected at inclusion of patients presenting treatment-resistant depression who will be responders at 6 months to those of non-responders, and to evaluate the predictive value of cognitive indicators on clinical therapeutic response at 6 months after a therapeutic modification. METHODS: Observational study. Patients were evaluated at the clinical (HDRS and BDI-II) and cognitive levels using standardized tools assessing memory, executive functions, attention, and social cognition, prior to a change in antidepressant treatment. Six months after inclusion, they were reassessed and classified into two groups based on the presence or absence of therapeutic response, defined by a 50% improvement on HDRS and BDI-II. The cognitive scores collected at inclusion were then compared. Additionally, univariate logistic regression models were used. RESULTS: Thirty patients were included in this study. Only 13 could be evaluated at 6 months. Among these patients, four had responded to the new treatment while nine were non-responders. Both groups of patients presented deviant cognitive performances compared to norms on tests evaluating executive functions and attention. Statistical analyses did not reveal any difference between the cognitive performances of responders and non-responders at 6 months. Regression analyses showed no association between cognitive scores and therapeutic response at 6 months. CONCLUSION: Executive functioning plays a significant role in treatment-resistant depression. In order to improve the understanding and identification of subtypes of depression, cognitive indicators should be systematically integrated into future research.

Treatment of Chronic Refractory Pain by Combined Deep Brain Stimulation of the Anterior Cingulum and Sensory Thalamus (EMOPAIN Study): Rationale and Protocol of a Feasibility and Safety Study.

BACKGROUND: Deep Brain Stimulation (DBS) of the sensory thalamus has been proposed for 40 years to treat medically refractory neuropathic pain, but its efficacy remains partial and unpredictable. Recent pilot studies of DBS targeting the ACC, a brain region involved in the integration of the affective, emotional, and cognitive aspects of pain, may improve patients suffering from refractory chronic pain. ACC-DBS could be complementary to thalamic DBS to treat both the sensory-discriminative and the affective components of chronic pain, but the safety of combined DBS, especially on cognition and affects, has not been studied. METHODS: We propose a prospective, randomized, double-blind, and bicentric study to evaluate the feasibility and safety of bilateral ACC-DBS combined with unilateral thalamic DBS in adult patients suffering from chronic unilateral neuropathic pain, refractory to medical treatment. After a study period of six months, there is a cross-over randomized phase to compare the efficacy (evaluated by pain intensity and quality of life) and safety (evaluated by repeated neurological examination, psychiatric assessment, cognitive assessment, and assessment of affective functions) of combined ACC-thalamic DBS and thalamic DBS only, respectively. DISCUSSION: The EMOPAIN study will show if ACC-DBS is a safe and effective therapy for patients suffering from chronic unilateral neuropathic pain, refractory to medical treatment. The design of the study will, for the first time, assess the efficacy of ACC-DBS combined with thalamic DBS in a blinded way.

Artificial intelligence to evaluate postoperative pain based on facial expression recognition.

BACKGROUND: Pain intensity evaluation by self-report is difficult and biased in non-communicating people, which may contribute to inappropriate pain management. The use of artificial intelligence (AI) to evaluate pain intensity based on automated facial expression analysis has not been evaluated in clinical conditions. METHODS: We trained and externally validated a deep-learning system (ResNet-18 convolutional neural network) to identify and classify 2810 facial expressions of 1189 patients, captured before and after surgery, according to their self-reported pain intensity using numeric rating scale (NRS, 0-10). AI performances were evaluated by accuracy (concordance between AI prediction and patient-reported pain intensity), sensitivity and specificity to diagnose pain ≥4/10 and ≥7/10. We then confronted AI performances with those of 33 nurses to evaluate pain intensity from facial expression in the same situation. RESULTS: In the external testing set (120 face images), the deep learning system was able to predict exactly the pain intensity among the 11 possible scores (0-10) in 53% of the cases with a mean error of 2.4 points. Its sensitivities to detect pain ≥4/10 and ≥7/10 were 89.7% and 77.5%, respectively. Nurses estimated the right NRS pain intensity with a mean accuracy of 14.9% and identified pain ≥4/10 and ≥7/10 with sensitivities of 44.9% and 17.0%. CONCLUSIONS: Subject to further improvement of AI performances through further training, these results suggest that AI using facial expression analysis could be used to assist physicians to evaluate pain and detect severe pain, especially in people not able to report appropriately their pain by themselves. SIGNIFICANCE: These original findings represent a major step in the development of a fully automated, rapid, standardized and objective method based on facial expression analysis to measure pain and detect severe pain.

One year double blind study of high vs low frequency subcallosal cingulate stimulation for depression.

Subcallosal Brodmann's Area 25 (Cg25) Deep Brain Stimulation (DBS) is a new promising therapy for treatment resistant major depressive disorder (TR-MDD). While different DBS stimulating parameters may have an impact on the efficacy and safety of the therapy, there is no data to support a protocol for optimal stimulation parameters for depression. Here we present a prospective multi-center double-blind randomized crossed-over 13-month study that evaluated the effects of High (130 Hz) vs Low (20 Hz) frequency Cg25 stimulation for nine patients with TR-MDD. Four out of nine patients achieved response criteria (≥40% reduction of symptom score) compared to mean baseline values at the end of the study. The mean percent change of MADRS score showed a similar improvement in the high and low frequency stimulation groups after 6 months of stimulation (-15.4 ± 21.1 and -14.7 ± 21.1 respectively). The mean effect at the end of the second period (6 months after cross-over) was higher than the first period (first 6 months of stimulation) in all patients (-23.4 ± 19.9 (n = 6 periods) and -13.0 ± 22 (n = 9 periods) respectively). At the end of the second period, the mean percent change of the MADRS scores improved more in the high than low frequency groups (-31.3 ± 19.3 (n = 4 patients) and -7.7 ± 10.9 (n = 2 patients) respectively). Given the small numbers, detailed statistical analysis is challenging. Nonetheless the results of this study suggest that long term high frequency stimulation might confer the best results. Larger scale, randomized double blind trials are needed in order to evaluate the most effective stimulation parameters.

Detection of activities of daily living impairment in Alzheimer's disease and mild cognitive impairment using information and communication technology.

BACKGROUND: One of the key clinical features of Alzheimer's disease (AD) is impairment in daily functioning. Patients with mild cognitive impairment (MCI) also commonly have mild problems performing complex tasks. Information and communication technology (ICT), particularly techniques involving imaging and video processing, is of interest in order to improve assessment. The overall aim of this study is to demonstrate that it is possible using a video monitoring system to obtain a quantifiable assessment of instrumental activities of daily living (IADLs) in AD and in MCI. METHODS: The aim of the study is to propose a daily activity scenario (DAS) score that detects functional impairment using ICTs in AD and MCI compared with normal control group (NC). Sixty-four participants over 65 years old were included: 16 AD matched with 10 NC for protocol 1 (P1) and 19 MCI matched with 19 NC for protocol 2 (P2). Each participant was asked to undertake a set of daily tasks in the setting of a "smart home" equipped with two video cameras and everyday objects for use in activities of daily living (8 IADLs for P1 and 11 for P2, plus 4 temporal execution constraints). The DAS score was then computed from quantitative and qualitative parameters collected from video recordings. RESULTS: In P1, the DAS score differentiated AD (DAS(AD,P1) = 0.47, 95% confidence interval [CI] 0.38-0.56) from NC (DAS(NC,P1) = 0.71, 95% CI 0.68-0.74). In P2, the DAS score differentiated MCI (DAS(MCI,P2) = 0.11, 95% CI 0.05-0.16) and NC (DAS(NC,P2) = 0.36, 95% CI 0.26-0.45). CONCLUSION: In conclusion, this study outlines the interest of a novel tool coming from the ICT world for the assessment of functional impairment in AD and MCI. The derived DAS scores provide a pragmatic, ecological, objective measurement which may improve the prediction of future dementia, be used as an outcome measurement in clinical trials and lead to earlier therapeutic intervention.

Determinants of multiple semantic priming: a meta-analysis and spike frequency adaptive model of a cortical network.

Recall and language comprehension while processing sequences of words involves multiple semantic priming between several related and/or unrelated words. Accounting for multiple and interacting priming effects in terms of underlying neuronal structure and dynamics is a challenge for current models of semantic priming. Further elaboration of current models requires a quantifiable and reliable account of the simplest case of multiple priming resulting from two primes on a target. The meta-analytic approach offers a better understanding of the experimental data from studies on multiple priming regarding the additivity pattern of priming. The meta-analysis points to the effects of prime-target stimuli onset asynchronies on the pattern of underadditivity, overadditivity, or strict additivity of converging activation from multiple primes. The modeling approach is then constrained by results of the meta-analysis. We propose a model of a cortical network embedding spike frequency adaptation, which allows frequency and time-dependent modulation of neural activity. Model results give a comprehensive understanding of the meta-analysis results in terms of dynamics of neuron populations. They also give predictions regarding how stimuli intensities, association strength, and spike frequency adaptation influence multiple priming effects.

Dopaminergic neuromodulation of semantic priming in a cortical network model.

Semantic priming between items stored and associated in memory underlies contextual recall. Response times to process a given target item are shorter when following presentation of a related prime item than when it is unrelated. The study of priming effects allows investigating the structure of semantic networks as a function of association strength and number of links relating the prime and target. Behavioral data from divided visual field experiments in healthy subjects show a variability in the magnitude of priming effects when the left or right hemisphere is primary involved. Data from schizophrenic patients also exhibit variability in priming magnitude compared to data from healthy subjects. Mathematical models of cortical networks allow theorists to understand the link between the physiology of single neurons and synapses and network behavior. Computational modelling can replicate electrophysiological recordings of cortical neurons in monkeys, that exhibit two types of task-related activity, 'retrospective' (related to a previously shown stimulus) and 'prospective' (related to a stimulus expected to appear, due to learned association between both stimuli). Experimental studies of associative priming report priming effects on behavioral data in both human and monkeys. Cortical network models can account for a large variety of priming effects observed in human, and for the dependence of retrospective activity on dopamine neuromodulation. Here, we investigate how variable levels of dopamine in a model of a cortical network can modulate prospective activity to vary the magnitude of semantic priming. We simulate a biologically realistic network of integrate and fire neurons to study the effects of dopaminergic neuromodulation of NMDA receptors of glutamatergic and gabaergic neurons on semantic priming dynamics. Results support the possibility that different levels of dopaminergic neuromodulation can subtend hemispheric differences in semantic priming, corresponding to focused priming in the left hemisphere and to extended priming in the right hemisphere. Furthermore, results can account for priming perturbations in schizophrenia depending on the level of dopamine.