Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations.

BACKGROUND: The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans. OBJECTIVES: The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor. METHODS: We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality. RESULTS: The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band. CONCLUSIONS: Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A high-performance neuroprosthesis for speech decoding and avatar control.

Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive1. Here we use high-density surface recordings of the speech cortex in a clinical-trial participant with severe limb and vocal paralysis to achieve high-performance real-time decoding across three complementary speech-related output modalities: text, speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate accurate and rapid large-vocabulary decoding with a median rate of 78 words per minute and median word error rate of 25%. For speech audio, we demonstrate intelligible and rapid speech synthesis and personalization to the participant's pre-injury voice. For facial-avatar animation, we demonstrate the control of virtual orofacial movements for speech and non-speech communicative gestures. The decoders reached high performance with less than two weeks of training. Our findings introduce a multimodal speech-neuroprosthetic approach that has substantial promise to restore full, embodied communication to people living with severe paralysis.

Cortical responses to auditory stimulation predict the prognosis of patients with disorders of consciousness.

OBJECTIVE: This study aimed to assess the prognosis of patients with disorders of consciousness (DoC) using auditory stimulation with electroencephalogram (EEG) recordings. METHODS: We enrolled 72 patients with DoC in the study, which involved subjecting patients to auditory stimulation while EEG responses were recorded. Coma Recovery Scale-Revised (CRS-R) scores and Glasgow Outcome Scale (GOS) were determined for each patient and followed up for three months. A frequency spectrum analysis was performed on the EEG recordings. Finally, the power spectral density (PSD) index was used to predict the prognosis of patients with DoC based on a support vector machine (SVM) model. RESULTS: Power spectral analyses revealed that the cortical response to auditory stimulation showed a decreasing trend with decreasing consciousness levels. Auditory stimulation-induced changes in absolute PSD at the delta and theta bands were positively correlated with the CRS-R and GOS scores. Furthermore, these cortical responses to auditory stimulation had a good ability to discriminate between good and poor prognoses of patients with DoC. CONCLUSIONS: Auditory stimulation-induced changes in the PSD were highly predictive of DoC outcomes. SIGNIFICANCE: Our findings showed that cortical responses to auditory stimulation may be an important electrophysiological indicator of prognosis in patients with DoC.

Altered functional connectivity between cortical networks associated with inhibitory control in trauma-exposed females.

Post-traumatic stress disorder (PTSD) is associated with impaired inhibitory control and alterations in large-scale brain network connectivity. However, few studies to date have examined the construct of inhibitory control as it relates to resting-state functional connectivity (rsFC) in a population with PTSD or trauma-exposure. The present study investigated the relationship between impaired inhibitory control and rsFC within the default mode network (DMN), central executive network (CEN), and salience network (SN) in a sample of females exposed to interpersonal trauma with and without PTSD (n = 67). Participants completed a classic Color-Word Stroop task as a measure of inhibitory control and two resting-state fMRI scans. We conducted voxelwise rsFC analyses with seed regions in the DMN, CEN, and SN and voxelwise linear regression analyses to examine the relationship between inhibitory control and rsFC of these networks across the sample. Better Stroop performance was negatively associated with total self-reported PTSD symptoms. An analysis of PTSD symptom clusters indicated that better Stroop performance was also associated with re-experiencing and hyperarousal symptoms, but not avoidance PTSD symptoms. Decreased coupling between the CEN and the DMN was associated with better inhibitory control in this sample of trauma-exposed females. These findings lend support to the hypothesis that efficient switching between these networks may contribute to better performance on cognitive and attentional tasks in trauma-exposed individuals.

Cortical reactivity to transcranial magnetic stimulation predicts risk of post-stroke delirium.

OBJECTIVE: Post-stroke delirium (PSD) is a frequent and with regard to outcome unfavorable complication in acute stroke. The neurobiological mechanisms predisposing to PSD remain poorly understood, and biomarkers predicting its risk have not been established. We tested the hypothesis that hypoexcitable or disconnected brain networks predispose to PSD by measuring brain reactivity to transcranial magnetic stimulation with electroencephalography (TMS-EEG). METHODS: We conducted a cross-sectional study in 33 acute stroke patients within 48 hours of stroke onset. Brain reactivity to single-pulse TMS of dorsolateral prefrontal cortex, primary motor cortex and superior parietal lobule of the right hemisphere was quantified by response intensity, effective connectivity, perturbational complexity index (PCIST), and natural frequency of the TMS-EEG response. PSD development was clinically tracked every 8 hours before and for 7 days following TMS-EEG. RESULTS: Fourteen patients developed PSD while 19 patients did not. The PSD group showed lower excitability, effective connectivity, PCIST and natural frequency compared to the non-PSD group. The maximum PCIST over all three TMS sites demonstrated largest classification accuracy with a ROC-AUC of 0.943. This effect was independent of lesion size, affected hemisphere and stroke severity. Maximum PCIST and maximum natural frequency correlated inversely with delirium duration. CONCLUSIONS: Brain reactivity to TMS-EEG can unravel brain network states of reduced excitability, effective connectivity, perturbational complexity and natural frequency that identify acute stroke patients at high risk for development of delirium. SIGNIFICANCE: Findings provide novel insight into the pathophysiology of pre-delirium brain states and may promote effective delirium prevention strategies in those patients at high risk.

Increased intrinsic and synaptic excitability of hypothalamic POMC neurons underlies chronic stress-induced behavioral deficits.

Chronic stress exposure induces maladaptive behavioral responses and increases susceptibility to neuropsychiatric conditions. However, specific neuronal populations and circuits that are highly sensitive to stress and trigger maladaptive behavioral responses remain to be identified. Here we investigate the patterns of spontaneous activity of proopiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) of the hypothalamus following exposure to chronic unpredictable stress (CUS) for 10 days, a stress paradigm used to induce behavioral deficits such as anhedonia and behavioral despair [1, 2]. CUS exposure increased spontaneous firing of POMC neurons in both male and female mice, attributable to reduced GABA-mediated synaptic inhibition and increased intrinsic neuronal excitability. While acute activation of POMC neurons failed to induce behavioral changes in non-stressed mice of both sexes, subacute (3 days) and chronic (10 days) repeated activation of POMC neurons was sufficient to induce anhedonia and behavioral despair in males but not females under non-stress conditions. Acute activation of POMC neurons promoted susceptibility to subthreshold unpredictable stress in both male and female mice. Conversely, acute inhibition of POMC neurons was sufficient to reverse CUS-induced anhedonia and behavioral despair in both sexes. Collectively, these results indicate that chronic stress induces both synaptic and intrinsic plasticity of POMC neurons, leading to neuronal hyperactivity. Our findings suggest that POMC neuron dysfunction drives chronic stress-related behavioral deficits.

Cerebral pCREB-dependent social behavioral adversities following a short-term exposure to obesogenic diets in young hamsters.

Excessive fat and sugar intake represents a risk towards the development of different pathologies, such as obesity, diabetes, sociability and memory deficits. Although the adolescence stage is a susceptible period for these and other risks, effects of energy-dense nutrients in such an age period have not been fully investigated. In the present study, neurobehavioral alterations following a 4-week exposure to either normal diet (ND) or high-fat diet (HFD) plus normal water (NW) or liquid sugar (LS) were evaluated in young hamsters. HFD + LS and ND + LS significantly reduced food intake and water consumption, which was, in the latter group, almost completely substituted by LS. All obesogenic diets accounted for increased abdominal fat and liver weight with respect to body weight (p < 0.05-0.001). Additionally, glucose levels notably increased (p < 0.0001) together with insulin and triglycerides in HFD + LS (p < 0.001) and ND + LS (p < 0.01) while cholesterol displayed only a moderate increase (p < 0.05) in HFD + NW and HFD + LS. Animals fed with HFD and/or LS exhibited impaired social memory plus increased winning percentages (0.05 < p < 0.01) during the tube test. Interestingly, these same treatments led to a down-regulation of phosphorylated cAMP Response-Element Binding Protein (pCREB) in HFD + NW (p < 0.0001) for all areas, but rather was upregulated (p < 0.05) in ND + LS of the amygdala. Overall, in view of a brief exposure to palatable foods interfering with normal metabolic and social memory activities, the downregulation of pCREB constitutes a key indicator of neurobehavioral deficits during obesogenic diets. Compensatory mechanisms may be also occurring in the amygdala that strongly regulates emotional states via connections with other limbic areas.

Poly-dipeptides produced from C9orf72 hexanucleotide repeats cause selective motor neuron hyperexcitability in ALS.

SignificanceThe GGGGCC hexanucleotide repeat expansion in the chromosome 9 open reading frame 72 (C9orf72) gene is the most common genetic cause of amyotrophic lateral sclerosis (ALS). Despite myriad studies on the toxic effects of poly-dipeptides produced from the C9orf72 repeats, the mechanisms underlying the selective hyperexcitability of motor cortex that characterizes the early stages of C9orf72 ALS patients remain elusive. Here, we show that the proline-arginine poly-dipeptides cause hyperexcitability in cortical motor neurons by increasing persistent sodium currents conducted by the Nav1.2/ß4 sodium channel complex, which is highly expressed in the motor cortex. These findings provide the basis for understanding how the C9orf72 mutation causes motor neuron hyperactivation that can lead to the motor neuron death in C9orf72 ALS.

Magnetoencephalography detects phase-amplitude coupling in Parkinson's disease.

To characterize Parkinson's disease, abnormal phase-amplitude coupling is assessed in the cortico-basal circuit using invasive recordings. It is unknown whether the same phenomenon might be found in regions other than the cortico-basal ganglia circuit. We hypothesized that using magnetoencephalography to assess phase-amplitude coupling in the whole brain can characterize Parkinson's disease. We recorded resting-state magnetoencephalographic signals in patients with Parkinson's disease and in healthy age- and sex-matched participants. We compared whole-brain signals from the two groups, evaluating the power spectra of 3 frequency bands (alpha, 8-12 Hz; beta, 13-25 Hz; gamma, 50-100 Hz) and the coupling between gamma amplitude and alpha or beta phases. Patients with Parkinson's disease showed significant beta-gamma phase-amplitude coupling that was widely distributed in the sensorimotor, occipital, and temporal cortices; healthy participants showed such coupling only in parts of the somatosensory and temporal cortices. Moreover, beta- and gamma-band power differed significantly between participants in the two groups (P < 0.05). Finally, beta-gamma phase-amplitude coupling in the sensorimotor cortices correlated significantly with motor symptoms of Parkinson's disease (P < 0.05); beta- and gamma-band power did not. We thus demonstrated that beta-gamma phase-amplitude coupling in the resting state characterizes Parkinson's disease.

Awake Craniotomy for Subcortical Brain Metastasis Beneath the Speech Center: A Technical Case Report.

BACKGROUND: To preserve language function, intraoperative functional brain mapping (IFBM) in and near the speech center is essential. CASE REPORT: We present a case of a 73-year-old right-handed woman with colon cancer. She presented with mild speech disturbance. Magnetic resonance imaging revealed a ringed enhancing lesion in the frontal operculum. The preservation of language function was critical; therefore, she underwent awake craniotomy using IFBM. Thus, the speech site was elicited by cortical electrical stimulation at the surface, near the location of the tumor. We made a safe corticotomy on the surface of the lesion and performed the resection of brain metastasis (BM) via a safety corridor. We achieved gross total resection of the BM while preserving the language function. After surgery, she recovered from speech disturbance. She returned to her normal life with improved language function. CONCLUSION: IFBM is a useful tool to undertake a safe approach via the speech center, avoiding permanent language deficits.

Parietal intrahemispheric source connectivity of resting-state electroencephalographic alpha rhythms is abnormal in Naïve HIV patients.

Previous evidence showed abnormal parietal sources of resting-state electroencephalographic (EEG) delta (< 4 Hz) and alpha (8-12 Hz) rhythms in treatment-Naïve HIV (Naïve HIV) subjects, as cortical neural synchronization markers in quiet wakefulness. Here, we tested the hypothesis that these local abnormalities may be related to functional cortical dysconnectivity as an oscillatory brain network disorder. The present EEG database regarded 128 Naïve HIV and 60 Healthy subjects. The eLORETA freeware estimated lagged linear EEG source connectivity (LLC). The area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification between Healthy and HIV individuals. Parietal intrahemispheric LLC solutions in alpha sources were abnormally lower in the Naïve HIV than in the control group. Furthermore, those abnormalities were greater in the Naïve HIV subgroup with executive and visuospatial deficits than the Naïve HIV subgroup with normal cognition. AUROC curves of those LLC solutions exhibited moderate/good accuracies (0.75-0.88) in the discrimination between the Naïve HIV individuals with executive and visuospatial deficits vs. Naïve HIV individuals with normal cognition and control individuals. In quiet wakefulness, Naïve HIV subjects showed clinically relevant abnormalities in parietal alpha source connectivity. HIV may alter a parietal "hub" oscillating at the alpha frequency in quiet wakefulness as a brain network disorder.

Positive alcohol expectancy and resting-state functional connectivity of the insula in problem drinking.

Positive alcohol expectancy (AE), a significant predictor of excessive alcohol consumption, is associated with heightened drinking motivation and reduced control. As the insula interacts with the limbic and prefrontal structures to integrate stimulus saliency, interoception, and cognitive control, the region may play a unique role in modulating AE. Here, we examined resting-state functional connectivity of the right and left insula in relation to AE in 180 adult drinkers. Whole-brain multiple regressions and path analysis were performed to delineate the inter-relationship between AE, insular connectivity, and drinking severity. We found that heightened AE was associated with diminished right insular connectivity with regions involved in negative emotion processing and self-control, including the amygdala, putamen, and ventromedial prefrontal cortex. In contrast, there was a positive relationship between AE and right insular connectivity with regions implicated in motivated responses to alcohol stimuli, including the superior parietal lobule, postcentral and superior frontal gyri. Path analysis showed that the two sets of right insular connectivity exhibited opposing associations with AE and that their net strength (i.e., "control minus motivation") was negatively correlated with AE and drinking severity. Analyses of the left insula seed, in contrast, did not yield regional connectivity in significant correlation with AE. These findings highlight the roles of right insula connectivity in motivational and regulatory processes that may differentially modulate drinking behavior. Recruitment of the motivational circuit and/or disengagement of the affective control circuit would be associated with heightened AE and heavier alcohol consumption.

Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm.

The detection of epileptic seizures from electroencephalogram (EEG) signals is traditionally performed by clinical experts through visual inspection. It is a long process, is error prone, and requires a highly trained expert. In this research, a new method is presented for seizure classification for EEG signals using a dual-tree complex wavelet transform (DT-CWT) and fast Fourier transform (FFT) coupled with a least square support vector machine (LS-SVM) classifier. In this method, each EEG signal is divided into four segments. Each segment is further split into smaller sub-segments. The DT-CWT is applied to decompose each sub-segment into detailed and approximation coefficients (real and imaginary parts). The obtained coefficients by the DT-CWT at each decomposition level are passed through an FFT to identify the relevant frequency bands. Finally, a set of effective features are extracted from the sub-segments, and are then forwarded to the LS-SVM classifier to classify epileptic EEGs. In this paper, two epileptic EEG databases from Bonn and Bern Universities are used to evaluate the extracted features using the proposed method. The experimental results demonstrate that the method obtained an average accuracy of 97.7% and 96.8% for the Bonn and Bern databases, respectively. The results prove that the proposed DT-CWT and FFT based features extraction is an effective way to extract discriminative information from brain signals. The obtained results are also compared to those by k-means and Naïve Bayes classifiers as well as with the results from the previous methods reported for classifying epileptic seizures and identifying the focal and non-focal EEG signals. The obtained results show that the proposed method outperforms the others and it is effective in detecting epileptic seziures in EEG signals. The technique can be adopted to aid neurologists to better diagnose neurological disorders and for an early seizure warning system.

Pathophysiological mechanisms of oromandibular dystonia.

Oromandibular dystonia (OMD) is a rare form of focal idiopathic dystonia. OMD was clinically identified at the beginning of the 20th century, and the main clinical features have been progressively described over the years. However, OMD has several peculiarities that still remain unexplained, including the high rate of oral trauma, which is often related to the onset of motor symptoms. The purpose of this paper was to formulate a hypothesis regarding the pathophysiology of OMD, starting from the neuroanatomical basis of the masticatory and facial systems and highlighting the features that differentiate this condition from other forms of focal idiopathic dystonia. We provide a brief review of the clinical and etiological features of OMD as well as neurophysiological and neuroimaging findings obtained from studies in patients with OMD. We discuss possible pathophysiological mechanisms underlying OMD and suggest that abnormalities in sensory input processing may play a prominent role in OMD pathophysiology, possibly triggering a cascade of events that results in sensorimotor cortex network dysfunction. Finally, we identify open questions that future studies should address, including the effect of abnormal sensory input processing and oral trauma on the peculiar neurophysiological abnormalities observed in OMD.

Scn1a gene reactivation after symptom onset rescues pathological phenotypes in a mouse model of Dravet syndrome.

Dravet syndrome is a severe epileptic encephalopathy caused primarily by haploinsufficiency of the SCN1A gene. Repetitive seizures can lead to endurable and untreatable neurological deficits. Whether this severe pathology is reversible after symptom onset remains unknown. To address this question, we generated a Scn1a conditional knock-in mouse model (Scn1a Stop/+) in which Scn1a expression can be re-activated on-demand during the mouse lifetime. Scn1a gene disruption leads to the development of seizures, often associated with sudden unexpected death in epilepsy (SUDEP) and behavioral alterations including hyperactivity, social interaction deficits and cognitive impairment starting from the second/third week of age. However, we showed that Scn1a gene re-activation when symptoms were already manifested (P30) led to a complete rescue of both spontaneous and thermic inducible seizures, marked amelioration of behavioral abnormalities and normalization of hippocampal fast-spiking interneuron firing. We also identified dramatic gene expression alterations, including those associated with astrogliosis in Dravet syndrome mice, that, accordingly, were rescued by Scn1a gene expression normalization at P30. Interestingly, regaining of Nav1.1 physiological level rescued seizures also in adult Dravet syndrome mice (P90) after months of repetitive attacks. Overall, these findings represent a solid proof-of-concept highlighting that disease phenotype reversibility can be achieved when Scn1a gene activity is efficiently reconstituted in brain cells.

Racial Discrimination and Resting-State Functional Connectivity of Salience Network Nodes in Trauma-Exposed Black Adults in the United States.

Importance: For Black US residents, experiences of racial discrimination are still pervasive and frequent. Recent empirical work has amplified the lived experiences and narratives of Black people and further documented the detrimental effects of racial discrimination on both mental and physical health; however, there is still a need for further research to uncover the mechanisms connecting experiences of racial discrimination with adverse health outcomes. Objective: To examine neurobiological mechanisms that may offer novel insight into the association of racial discrimination with adverse health outcomes. Design, Setting, and Participants: This cross-sectional study included 102 Black adults who had recently experienced a traumatic injury. In the acute aftermath of the trauma, participants underwent a resting-state functional magnetic resonance imaging scan. Individuals were recruited from the emergency department at a Midwestern level 1 trauma center in the United States between March 2016 and July 2020. Data were analyzed from February to May 2021. Exposures: Self-reported lifetime exposure to racial discrimination, lifetime trauma exposure, annual household income, and current posttraumatic stress disorder (PTSD) symptoms were evaluated. Main Outcomes and Measures: Seed-to-voxel analyses were conducted to examine the association of racial discrimination with connectivity of salience network nodes (ie, amygdala and anterior insula). Results: A total of 102 individuals were included, with a mean (SD) age of 33 (10) years and 58 (57%) women. After adjusting for acute PTSD symptoms, annual household income, and lifetime trauma exposure, greater connectivity between the amygdala and thalamus was associated with greater exposure to discrimination (t(97) = 6.05; false discovery rate (FDR)-corrected P = .03). Similarly, racial discrimination was associated with greater connectivity between the insula and precuneus (t(97) = 4.32; FDR-corrected P = .02). Conclusions and Relevance: These results add to the mounting literature that racial discrimination is associated with neural correlates of vigilance and hyperarousal. The study findings extend this theory by showing that this association is apparent even when accounting for socioeconomic position, lifetime trauma, and symptoms of psychological distress related to an acute trauma.

Cortical Correlates of Gait Compensation Strategies in Parkinson Disease.

OBJECTIVE: Gait impairment in persons with Parkinson disease is common and debilitating. Compensation strategies (eg, external cues) are an essential part of rehabilitation, but their underlying mechanisms remain unclear. Using electroencephalography (EEG), we explored the cortical correlates of 3 categories of strategies: external cueing, internal cueing, and action observation. METHODS: Eighteen participants with Parkinson disease and gait impairment were included. We recorded 126-channel EEG during both stance and gait on a treadmill under 4 conditions: (1) uncued, (2) external cueing (listening to a metronome), (3) internal cueing (silent rhythmic counting), and (4) action observation (observing another person walking). To control for the effects of sensory processing of the cues, we computed relative power changes as the difference in power spectral density between walking and standing for each condition. RESULTS: Relative to uncued gait, the use of all 3 compensation strategies induced a decrease of beta band activity in sensorimotor areas, indicative of increased cortical activation. Parieto-occipital alpha band activity decreased with external and internal cueing, and increased with action observation. Only internal cueing induced a change in frontal cortical activation, showing a decrease of beta band activity compared to uncued gait. INTERPRETATION: The application of compensation strategies resulted in changed cortical activity compared to uncued gait, which could not be solely attributed to sensory processing of the cueing modality. Our findings suggest there are multiple routes to control gait, and different compensation strategies seem to rely on different cortical mechanisms to achieve enhanced central motor activation in persons with Parkinson disease. ANN NEUROL 2022;91:329-341.

Differences in cortical processing of facial emotions in broader autism phenotype.

Autism Spectrum Disorder (ASD) is a heterogeneous condition that affects face perception. Evidence shows that there are differences in face perception associated with the processing of low spatial frequency (LSF) and high spatial frequency (HSF) of visual stimuli between non-symptomatic relatives of individuals with autism (broader autism phenotype, BAP) and typically developing individuals. However, the neural mechanisms involved in these differences are not fully understood. Here we tested whether face-sensitive event related potentials could serve as neuronal markers of differential spatial frequency processing, and whether these potentials could differentiate non-symptomatic parents of children with autism (pASD) from parents of typically developing children (pTD). To this end, we performed electroencephalographic recordings of both groups of parents while they had to recognize emotions of face pictures composed of the same or different emotions (happiness or anger) presented in different spatial frequencies. We found no significant differences in the accuracy between groups but lower amplitude modulation in the Late Positive Potential activity in pASD. Source analysis showed a difference in the right posterior part of the superior temporal region that correlated with ASD symptomatology of the child. These results reveal differences in brain processing of recognition of facial emotion in BAP that could be a precursor of ASD.

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning.

Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8-12 Hz) and beta oscillations (13-30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.

High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways.

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100,000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.