Personalized, Multisession, Multichannel Transcranial Direct Current Stimulation in Medication-Refractory Focal Epilepsy: An Open-Label Study.

PURPOSE: Animal and proof-of-principle human studies suggest that cathodal transcranial direct current stimulation may suppress seizures in drug-resistant focal epilepsy. The present study tests the safety, tolerability, and effect size of repeated daily cathodal transcranial direct current stimulation in epilepsy have not been established, limiting development of clinically meaningful interventions. METHODS: We conducted a 2-center, open-label study on 20 participants with medically refractory, focal epilepsy, aged 9 to 56 years (11 women and 9 children younger than18 years). Each participant underwent 10 sessions of 20 minutes of cathodal transcranial direct current stimulation over 2 weeks. Multielectrode montages were designed using a realistic head model-driven approach to conduct an inhibitory electric field to the target cortical seizure foci and surrounding cortex to suppress excitability and reduce seizure rates. Patients recorded daily seizures using a seizure diary 8 weeks prior, 2 weeks during, and 8 to 12 weeks after the stimulation period. RESULTS: The median seizure reduction was 44% relative to baseline and did not differ between adult and pediatric patients. Three patients experienced an increase in seizure frequency of >50% during the stimulation period; in one, a 36% increase in seizure frequency persisted through 12 weeks of follow-up. Otherwise, participants experienced only minor adverse events-the most common being scalp discomfort during transcranial direct current stimulation. CONCLUSIONS: This pilot study supports the safety and efficacy of multifocal, personalized, multichannel, cathodal transcranial direct current stimulation for adult and pediatric patients with medication-refractory focal epilepsy, although identifies a possibility of seizure exacerbation in some. The data also provide insight into the effect size to inform the design of a randomized, sham-stimulation controlled trial.

Locally Grown Crops and Immunocastration in Fattening Heavy Pigs: Effects on Performance and Welfare.

This study aimed to explore dietary replacement soybean meal (SBM) with local pea seeds (PS-L) and the effects of surgically castrated (CM) or immunocastration (IM) in heavy male pigs, on growth performance, feeding behaviour, and tail and ear lesions. Four treatments were arranged factorially (2 × 2), with two sexes and two dietary treatments (96 pigs in eight pens). The inclusion of PS-L was 25%, 30%, and 40% during three phases (40−80 kg, 80−110 kg, and 110−140 kg, respectively). No difference in average daily feed intake (ADFI) and body-weight (BW) between PS-L and SBM could be demonstrated (p > 0.05), but PS-L diet decreased the average daily gain (ADG) at 110−140 kg of BW (p < 0.001) and increased feed conversion rate (FCR) (p < 0.05). The ADG was higher (p < 0.01) in IM than CM in all fattening periods, and the FCR in IM was lower (p < 0.05) than in barrows. IM pigs had lower dressing percentage than CM (p < 0.01). Pigs fed a PS-L diet ate faster but increased their passive behaviour compared with those fed SBM. In conclusion, the PS-L diet did not reduce BW and improved passive behaviour, and IM grew more efficiently, regardless of diet.

Transcranial Direct Current Stimulation to the Left Dorsolateral Prefrontal Cortex Improves Cognitive Control in Patients With Attention-Deficit/Hyperactivity Disorder: A Randomized Behavioral and Neurophysiological Study.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with significant morbidity and mortality that may affect over 5% of children and approximately 2.8% of adults worldwide. Pharmacological and behavioral therapies for ADHD exist, but critical symptoms such as dysexecutive deficits remain unaffected. In a randomized, sham-controlled, double-blind, crossover mechanistic study, we assessed the cognitive and physiological effects of transcranial direct current stimulation (tDCS) in 40 adult patients with ADHD in order to identify diagnostic (cross-sectional) and treatment biomarkers (targets). METHODS: Patients performed three experimental sessions in which they received 30 minutes of 2 mA anodal tDCS targeting the left dorsolateral prefrontal cortex, 30 minutes of 2 mA anodal tDCS targeting the right dorsolateral prefrontal cortex, and 30 minutes of sham. Before and after each session, half the patients completed the Eriksen flanker task and the other half completed the stop signal task while we assessed behavior (reaction time, accuracy) and neurophysiology (event-related potentials). RESULTS: Anodal tDCS to the left dorsolateral prefrontal cortex modulated cognitive (reaction time) and physiological (P300 amplitude) measures in the Eriksen flanker task in a state-dependent manner, but no effects were found in the stop signal reaction time of the stop signal task. CONCLUSIONS: These findings show procognitive effects in ADHD associated with the modulation of event-related potential signatures of cognitive control, linking target engagement with cognitive benefit, proving the value of event-related potentials as cross-sectional biomarkers of executive performance, and mechanistically supporting the state-dependent nature of tDCS. We interpret these results as an improvement in cognitive control but not action cancellation, supporting the existence of different impulsivity constructs with overlapping but distinct anatomical substrates, and highlighting the implications for the development of individualized therapeutics.

Deep Learning Convolutional Neural Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related Spectral EEG.

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, and with the key advantage, compared with other machine learning approaches, of avoiding the need for manual selection of EEG spectral or channel features. The event-related spectrograms also provide greater accuracy compared to resting state EEG spectrograms. Finally, through the use of feature visualization techniques such as DeepDream, we show that the main features exciting the CNN nodes are a decreased power in the alpha band and an increased power in the delta-theta band around 100 ms for ADHD patients compared to healthy controls, suggestive of attentional and inhibition deficits, which have been previously suggested as pathophyisiological signatures of ADHD. While confirmation with larger clinical samples is necessary, these results suggest that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, highlighting the potential of this methodology to develop biomarkers of practical clinical utility.

Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder.

REM Behavior Disorder (RBD) is now recognized as the prodromal stage of α-synucleinopathies such as Parkinson's disease (PD). In this paper, we describe deep learning models for diagnosis/prognosis derived from a few minutes of eyes-closed resting electroencephalography data (EEG) collected at baseline from idiopathic RBD patients (n = 121) and healthy controls (HC, n = 91). A few years after the EEG acquisition (4±2 years), a subset of the RBD patients were eventually diagnosed with either PD (n = 14) or Dementia with Lewy bodies (DLB, n = 13), while the rest remained idiopathic RBD. We describe first a simple deep convolutional neural network (DCNN) with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms from idiopathic patients and healthy controls. We treat the data as in audio or image classification problems where deep networks have proven successful by exploiting invariances and compositional features in the data. For comparison, we study a simple deep recurrent neural network (RNN) model using three stacked Long Short Term Memory network (LSTM) cells or gated-recurrent unit (GRU) cells-with very similar results. The performance of these networks typically reaches 80% (±1%) classification accuracy in the balanced HC vs. PD-conversion classification problem. In particular, using data from the best single EEG channel, we obtain an area under the curve (AUC) of 87% (±1%)-while avoiding spectral feature selection. The trained classifier can also be used to generate synthetic spectrograms using the DeepDream algorithm to study what time-frequency features are relevant for classification. We find these to be bursts in the theta band together with a decrease of bursting in the alpha band in future RBD converters (i.e., converting to PD or DLB in the follow up) relative to HCs. From this first study, we conclude that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, offering physiological insights and enabling the identification of clinically relevant biomarkers.

tDCS to the left DLPFC modulates cognitive and physiological correlates of executive function in a state-dependent manner.

BACKGROUND: The use of transcranial Direct Current Stimulation (tDCS) to study anatomical and physiological dynamics and circuits supporting cognition and executive functions in particular has dramatically increased in recent years. However, its mechanisms of action remain only partially understood. OBJECTIVE: In this study we assess the cognitive and physiological effects of anodal tDCS to the DLPFC on executive function in order to understand (1) the role of DLPFC laterality, (2) the physiological dynamics sustaining the modulation of executive function by tDCS, and (3) the impact of state-dependent dynamics. METHODS: In a randomized, placebo-controlled, cross-over study, we applied anodal tDCS targeting the left vs. right DLPFC vs. sham in 20 healthy individuals (10 males, 10 females). Immediately before and after tDCS, subjects performed the Flanker Task while we measured behavioral (reaction time and accuracy) and neurophysiological (ERP) responses. Specifically, the amplitude of N200, P300, ERN and Pe is compared before and after stimulation. RESULTS: Anodal tDCS to the left DLPFC lead to a significant improvement in reaction time, an increase in P300 amplitude and a decrease in N200 amplitude in a state-dependent manner: baseline ERP amplitudes conditioned the effects of tDCS. CONCLUSION: Given the role of these ERPs in conflict-related tasks, we speculate that tDCS is modulating the subconstructs of selective attention, conflict monitoring and response inhibition. These findings contribute to a further understanding of the role of left DLPFC in the modulation of executive function, and shed light into the mechanisms of action and the state dependent nature of tDCS.