Individual Brain Responses Research Articles

Finding neural correlates of depersonalisation/derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores

Mental health disorders are typically diagnosed based on subjective reports (e.g., through questionnaires) followed by clinical interviews to evaluate the self-reported symptoms. Therefore, considering the interconnected nature of psychiatric disorders, their accurate diagnosis is a real challenge without indicators of underlying physiological dysfunction. Depersonalisation/derealisation disorder (DPD) is an example of dissociative disorder affecting 1–2 % of the population. DPD is characterised mainly by persistent disembodiment, detachment from surroundings, and feelings of emotional numbness, which can significantly impact patients' quality of life. The underlying neural correlates of DPD have been investigated for years to understand and help with a more accurate and in-time diagnosis of the disorder. However, in terms of EEG studies, which hold great importance due to their convenient and inexpensive nature, the literature has often been based on hypotheses proposed by experts in the field, which require prior knowledge of the disorder. In addition, participants' labelling in research experiments is often derived from the outcome of the Cambridge Depersonalisation Scale (CDS), a subjective assessment to quantify the level of depersonalisation/derealisation, the threshold and reliability of which might be challenged. As a result, we aimed to propose a novel end-to-end EEG processing pipeline based on deep neural networks for DPD biomarker discovery, which requires no prior handcrafted labelled data. Alternatively, it can assimilate knowledge from clinical outcomes like CDS as well as data-driven patterns that differentiate individual brain responses. In addition, the structure of the proposed model targets the uncertainty in CDS scores by using them as prior information only to guide the unsupervised learning task in a multi-task learning scenario. A comprehensive evaluation has been done to confirm the significance of the proposed deep structure, including new ways of network visualisation to investigate spectral, spatial, and temporal information derived in the learning process. We argued that the proposed EEG analytics could also be applied to investigate other psychological and mental disorders currently indicated on the basis of clinical assessment scores. The code to reproduce the results presented in this paper is openly accessible at https://github.com/AbbasSalami/DPD_Analysis.

Cortical responses to social stimuli in infants at elevated likelihood of ASD and/or ADHD: A prospective cross-condition fNIRS study

Autism spectrum disorders (ASD) and attention-deficit hyperactivity disorder (ADHD) are highly prevalent neurodevelopmental conditions that often co-occur and present both common and distinct neurodevelopmental profiles. Studying the developmental pathways leading to the emergence of ASD and/or ADHD symptomatology is crucial in understanding neurodiversity and discovering the mechanisms that underpin it. This study used functional near-infrared spectroscopy (fNIRS) to investigate differences in cortical specialization to social stimuli between 4- to 6-month-old infants at typical and elevated likelihood of ASD and/or ADHD. Results showed that infants at both elevated likelihood of ASD and ADHD had reduced selectivity to vocal sounds in left middle and superior temporal gyrus. Furthermore, infants at elevated likelihood of ASD showed attenuated responses to visual social stimuli in several cortical regions compared to infants at typical likelihood. Individual brain responses to visual social stimuli were associated with later autism traits, but not ADHD traits. These outcomes support our previous observations showing atypical social brain responses in infants at elevated likelihood of ASD and align with later atypical brain responses to social stimuli observed in children and adults with ASD. These findings highlight the importance of characterizing antecedent biomarkers of atypicalities in processing socially relevant information that might contribute to both phenotypic overlap and divergence across ASD and ADHD conditions and their association with the later emergence of behavioural symptoms.

Trait mindful awareness predicts inter-brain coupling but not individual brain responses during naturalistic face-to-face interactions.

In recent years, the possible benefits of mindfulness meditation have sparked much public and academic interest. Mindfulness emphasizes cultivating awareness of our immediate experience and has been associated with compassion, empathy, and various other prosocial traits. However, neurobiological evidence pertaining to the prosocial benefits of mindfulness in social settings is sparse. In this study, we investigate neural correlates of trait mindful awareness during naturalistic dyadic interactions, using both intra-brain and inter-brain measures. We used the Muse headset, a portable electroencephalogram (EEG) device often used to support mindfulness meditation, to record brain activity from dyads as they engaged in naturalistic face-to-face interactions in a museum setting. While we did not replicate prior laboratory-based findings linking trait mindfulness to individual brain responses (N = 379 individuals), self-reported mindful awareness did predict dyadic inter-brain synchrony, in theta (~5–8 Hz) and beta frequencies (~26-27 Hz; N = 62 dyads). These findings underscore the importance of conducting social neuroscience research in ecological settings to enrich our understanding of how (multi-brain) neural correlates of social traits such as mindful awareness manifest during social interaction, while raising critical practical considerations regarding the viability of commercially available EEG systems.

Cortical responses to noninvasive perturbations enable individual brain fingerprinting

BackgroundIn recent years, it has become increasingly apparent that characterizing individual brain structure, connectivity and dynamics is essential for understanding brain function in health and disease. However, the majority of neuroimaging and brain stimulation research has characterized human brain function by averaging measurements from groups of subjects and providing population-level inferences. External perturbations applied directly to well-defined brain regions can reveal distinctive information about the state, connectivity and dynamics of the human brain at the individual level. ObjectivesIn a series of studies, we aimed to characterize individual brain responses to MRI-guided transcranial magnetic stimulation (TMS), and explore the reproducibility of the evoked effects, differences between brain regions, and their individual specificity. MethodsIn the first study, we administered single pulses of TMS to both anatomically (left dorsolateral prefrontal cortex- ‘L-DLPFC’, left Intra-parietal lobule- ‘L-IPL) and functionally (left motor cortex- ‘L-M1’, right default mode network- ‘R-DMN, right dorsal attention network- ‘R-DAN’) defined cortical nodes in the frontal, motor, and parietal regions across two identical sessions spaced one month apart in 24 healthy volunteers. In the second study, we extended our analyses to two independent data sets (n = 10 in both data sets) having different sham-TMS protocols. ResultsIn the first study, we found that perturbation-induced cortical propagation patterns are heterogeneous across individuals but highly reproducible within individuals, specific to the stimulated region, and distinct from spontaneous activity. Most importantly, we demonstrate that by assessing the spatiotemporal characteristics of TMS-induced brain responses originating from different cortical regions, individual subjects can be identified with perfect accuracy. In the second study, we demonstrated that subject specificity of TEPs is generalizable across independent data sets and distinct from non-transcranial neural responses evoked by sham-TMS protocols. ConclusionsPerturbation-induced brain responses reveal unique “brain fingerprints” that reflect causal connectivity dynamics of the stimulated brain regions, and may serve as reliable biomarkers of individual brain function.

Native language inhibition predicts more successful second language learning: Evidence of two ERP pathways during learning

How some individuals succeed in learning a second language as adults is still an unsolved question in cognitive neuroscience. At the brain level, adults' electrophysiological responses to input in a second language may differ after completing different types of training. However, there is limited understanding of what neural pathways are activated as learning unfolds, and which patterns of activation lead to successful learning. Using brain event-related potentials, this study explored whether individual brain responses to practice difficulty during second language learning predict learning outcomes. English-speaking learners of Spanish practiced completing newly learned phrases in their second language. For some learners, all the choices presented during practice were “easy” because non-target choices were unrelated distractors. In the more “difficult” practice mode, however, learners had to avoid choosing a competing word that would be acceptable based on their native language, but not in the second language being learned. Performance during practice was similar in both groups of learners. Critically, divergence in event-related potentials indicated alternative strategies to practice, based on the level of difficulty. At the group level, learners completing the easier practice revealed increased monitoring when making responses; in the difficult condition, learners showed inhibition of their native language (i.e., an N400 for phrases congruent with the native language) to avoid interference during word selection. Individual brain responses indexing the degree of native language inhibition predicted learning rates in tests.

Individualized psychiatric imaging based on inter-subject neural synchronization in movie watching

The individual heterogeneity is a challenge to the prosperous promises of cutting-edge neuroimaging techniques for better diagnosis and early detection of psychiatric disorders. Individuals with similar clinical manifestations may result from very different pathophysiology. Conventional approaches based on comparing group-averages provide insufficient information to support the individualized diagnosis. Here we present an individualized imaging methodology that combines naturalistic imaging and the normative model. This paradigm adopts video clips with rich cognitive, social, and emotional contents to evoke synchronized brain dynamics of healthy participants and builds a spatiotemporal response norm. By comparing individual brain responses with the response norm, we could recognize patients using machine learning techniques. We applied this methodology to recognize first-episode drug-naïve schizophrenia patients in a dataset containing 72 patients and 54 healthy controls. Some segments of the video evoked more synchronized brain activity in the healthy controls than in the schizophrenia patients. We built a spatiotemporal response norm by averaging the brain responses of the healthy controls in a training set, and trained a classifier to recognize patients based on the differences between individual brain responses and the norm. The performance of the classifier was then evaluated using an independent test set. The mean accuracies from a 5-fold cross-validation were 0.71–0.78 depending on the parameters such as the number of features and the width of the sliding windows. These findings reflected the potential of this methodology towards a clinical tool for individualized diagnosis.

Quantifying the individual auditory and visual brain response in 7-month-old infants watching a brief cartoon movie

Electroencephalography (EEG) continues to be the most popular method to investigate cognitive brain mechanisms in young children and infants. Most infant studies rely on the well-established and easy-to-use event-related brain potential (ERP). As a severe disadvantage, ERP computation requires a large number of repetitions of items from the same stimulus-category, compromising both ERPs’ reliability and their ecological validity in infant research. We here explore a way to investigate infant continuous EEG responses to an ongoing, engaging signal (i.e., “neural tracking”) by using multivariate temporal response functions (mTRFs), an approach increasingly popular in adult EEG research. N = 52 infants watched a 5-min episode of an age-appropriate cartoon while the EEG signal was recorded. We estimated and validated forward encoding models of auditory-envelope and visual-motion features. We compared individual and group-based (‘generic’) models of the infant brain response to comparison data from N = 28 adults. The generic model yielded clearly defined response functions for both, the auditory and the motion regressor. Importantly, this response profile was present also on an individual level, albeit with lower precision of the estimate but above-chance predictive accuracy for the modelled individual brain responses. In sum, we demonstrate that mTRFs are a feasible way of analyzing continuous EEG responses in infants. We observe robust response estimates both across and within participants from only 5 min of recorded EEG signal. Our results open ways for incorporating more engaging and more ecologically valid stimulus materials when probing cognitive, perceptual, and affective processes in infants and young children.

ID 379 – Individual operculo-insular responses to painful heat and non-painful heat stimulations with 3Tesla-fMRI

Objective To assess individual brain responses to either painful heat or non-painful heat stimuli in the operculo-insular cortices. To determine whether painful heat stimulations activate specific parts of the insula or whether insula was activated unspecifically by painful and non-painful heat stimulations. Methods Heat stimulations were applied on the back of 20 subjects’ left hand. Twenty-four stimulations (7 s duration) were applied for each session, interleaved with rest periods which duration was randomized. Regions of interest (ROIs) of the MNI template were transformed to match patients’ brain. Within each ROI, intensity of activations and number of activated voxels were statistically compared for painful and non-painful stimulations. Results For painful heat stimulations, contralateral and ipsilateral insulae were activated almost consistently across subjects, especially in the anterior part. For non-painful heat stimulations, both intensity of activation and number of activated voxels are less consistent, located in the same subdivisions but mainly contralateral to stimulation. Conclusions Painful heat stimulations involve quite specifically the ipsilateral insula. Anterior insula is the most consistently activated area with a higher intensity of activation for painful heat as compared to non-painful heat stimulations. Key message Individual fRMI responses to heat pain prevail in anterior insulae and associate bilateral responses.

Individual brain responses to taste cluster into 5 distinct patterns related to dynamic liking

Individual brain responses to taste cluster into 5 distinct patterns related to dynamic liking

Ketamine effects on memory reconsolidation favor a learning model of delusions.

Delusions are the persistent and often bizarre beliefs that characterise psychosis. Previous studies have suggested that their emergence may be explained by disturbances in prediction error-dependent learning. Here we set up complementary studies in order to examine whether such a disturbance also modulates memory reconsolidation and hence explains their remarkable persistence. First, we quantified individual brain responses to prediction error in a causal learning task in 18 human subjects (8 female). Next, a placebo-controlled within-subjects study of the impact of ketamine was set up on the same individuals. We determined the influence of this NMDA receptor antagonist (previously shown to induce aberrant prediction error signal and lead to transient alterations in perception and belief) on the evolution of a fear memory over a 72 hour period: they initially underwent Pavlovian fear conditioning; 24 hours later, during ketamine or placebo administration, the conditioned stimulus (CS) was presented once, without reinforcement; memory strength was then tested again 24 hours later. Re-presentation of the CS under ketamine led to a stronger subsequent memory than under placebo. Moreover, the degree of strengthening correlated with individual vulnerability to ketamine's psychotogenic effects and with prediction error brain signal. This finding was partially replicated in an independent sample with an appetitive learning procedure (in 8 human subjects, 4 female). These results suggest a link between altered prediction error, memory strength and psychosis. They point to a core disruption that may explain not only the emergence of delusional beliefs but also their persistence.

Effects of nicotine withdrawal on verbal working memory and associated brain response

Previous literature has reported effects of nicotine withdrawal on brain function during cognitive tasks such as verbal working memory (VWM). Mechanisms of these withdrawal effects have not been clearly identified. Functional neuroimaging offers an objective method to examine brain mechanisms associated with observable behavior and subjective reports. To investigate these mechanisms, 12 smokers were administered a 2-Back VWM challenge during two functional magnetic resonance imaging sessions. Participants abstained from smoking prior to both sessions; however, they applied a nicotine patch before one session and a placebo patch prior to the other. Among regions that exhibited a significant response to the 2-Back during either session, withdrawal was associated with significantly greater deactivation in left and right temporal poles and left medial frontal gyrus. The magnitude of task-related activation showed a significant inverse relationship to craving in the majority of regions during placebo administration. Also, individual brain responses varied more during placebo, suggesting inefficient neural processing. Results suggest that differences in brain response to a VWM challenge during abstinence may be attributed to increased craving. Further deactivation of regions associated with the default network (medial frontal and anterior temporal clusters) during the placebo condition suggests further suspension of default activity, possibly to compensate for inefficient neural processing.