Neural circuits underlying a psychotherapeutic regimen for fear disorders.

A psychotherapeutic regimen that uses alternating bilateral sensory stimulation (ABS) has been used to treat post-traumatic stress disorder. However, the neural basis that underlies the long-lasting effect of this treatment-described as eye movement desensitization and reprocessing-has not been identified. Here we describe a neuronal pathway driven by the superior colliculus (SC) that mediates persistent attenuation of fear. We successfully induced a lasting reduction in fear in mice by pairing visual ABS with conditioned stimuli during fear extinction. Among the types of visual stimulation tested, ABS provided the strongest fear-reducing effect and yielded sustained increases in the activities of the SC and mediodorsal thalamus (MD). Optogenetic manipulation revealed that the SC-MD circuit was necessary and sufficient to prevent the return of fear. ABS suppressed the activity of fear-encoding cells and stabilized inhibitory neurotransmission in the basolateral amygdala through a feedforward inhibitory circuit from the MD. Together, these results reveal the neural circuit that underlies an effective strategy for sustainably attenuating traumatic memories.

Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Effect of Ball Milling on Electrochemical Properties of Sulfur/Polyacrylonitrile (SPAN) Cathode in Li/S Battery.

Ball-milling process was applied to increase sulfur content in sulfur/polyacrylonitrile (SPAN) composites and improve electrochemical properties of Li/S batteries. In contrast to as-received PAN, pre-heated PAN was pulverized by the ball-milling, resulting in the continuous size reduction with increasing the milling time. Sulfur contents in SPAN composites synthesized with a pre-heated and milled PAN were increased with prolonging the milling time and the maximum content reached 44.5% for the milling time of 10 h. Li/S cells with SPAN electrodes delivered the first discharge capacities of 1356, 1409, 1512, and 1610 mAh/g-sulfur for milling times of 0, 1, 5, and 10 h. The 10 h-milled SPAN electrode with the highest sulfur content exhibited poor initial efficiency and low capacity retention at 100 cycles, whereas from a comprehensive viewpoint of the specific capacity and capacity retention, the 6 h-milled SPAN electrode exhibited the best electrochemical performance due to the suitable size and sulfur content.

Neural dynamics of two players when using nonverbal cues to gauge intentions to cooperate during the Prisoner's Dilemma Game.

Social interaction is a fundamental part of our daily lives; however, exactly how our brains use social cues to determine whether to cooperate without being exploited remains unclear. In this study, we used an electroencephalography (EEG) hyperscanning approach to investigate the effect of face-to-face contact on the brain mechanisms underlying the decision to cooperate or defect in an iterated version of the Prisoner's Dilemma Game. Participants played the game either in face-to-face or face-blocked conditions. The face-to-face interaction led players to cooperate more often, providing behavioral evidence for the use of these nonverbal cues in their social decision-making. In addition, the EEG hyperscanning identified temporal dynamics and inter-brain synchronization across the cortex, providing evidence for involvement of these regions in the processing of face-to-face cues to read each other's intent to cooperate. Most notably, the power of the alpha frequency band (8-13Hz) in the right temporoparietal region immediately after seeing a round outcome significantly differed between face-to-face and face-blocked conditions and predicted whether an individual would adopt a 'cooperation' or 'defection' strategy. Moreover, inter-brain synchronies within this time and frequency range reflected the use of these strategies. This study provides evidence for how the cortex uses nonverbal social cues to determine other's intentions, and highlights the significance of power in the alpha band and inter-brain phase synchronizations in high-level socio-cognitive processing.

Vulnerability-Based Critical Neurons, Synapses, and Pathways in the Caenorhabditis elegans Connectome.

Determining the fundamental architectural design of complex nervous systems will lead to significant medical and technological advances. Yet it remains unclear how nervous systems evolved highly efficient networks with near optimal sharing of pathways that yet produce multiple distinct behaviors to reach the organism's goals. To determine this, the nematode roundworm Caenorhabditis elegans is an attractive model system. Progress has been made in delineating the behavioral circuits of the C. elegans, however, many details are unclear, including the specific functions of every neuron and synapse, as well as the extent the behavioral circuits are separate and parallel versus integrative and serial. Network analysis provides a normative approach to help specify the network design. We investigated the vulnerability of the Caenorhabditis elegans connectome by performing computational experiments that (a) "attacked" 279 individual neurons and 2,990 weighted synaptic connections (composed of 6,393 chemical synapses and 890 electrical junctions) and (b) quantified the effects of each removal on global network properties that influence information processing. The analysis identified 12 critical neurons and 29 critical synapses for establishing fundamental network properties. These critical constituents were found to be control elements-i.e., those with the most influence over multiple underlying pathways. Additionally, the critical synapses formed into circuit-level pathways. These emergent pathways provide evidence for (a) the importance of backward locomotion, avoidance behavior, and social feeding behavior to the organism; (b) the potential roles of specific neurons whose functions have been unclear; and

Impact of childhood trauma and cognitive emotion regulation strategies on risk-aversive and loss-aversive patterns of decision-making in patients with depression.

INTRODUCTION: Although poor decision-making ultimately impairs quality of life in depression, few studies describe the clinical characteristics of patients suffering from dysfunctional decision-making. This study aims to delineate the effect of childhood trauma and other personality factors on risk-aversive and loss-aversive patterns of decision-making in patients with depression. METHODS: A total of 50 depressive patients completed surveys for the measurement of sociodemographic factors, trauma loads and other clinical characteristics, including depression, anxiety, and strategies for emotion regulation. Risk aversion and loss aversion were quantified using probability discounting task and a 50:50 gamble on monetary decision-making task under specified risks. Stepwise multiple regression analysis was performed to determine the factors, predicting risk aversion or loss aversion in depression. RESULTS: Childhood trauma was the most prominent factor predicting loss aversion in patients with depressive disorders. Overall maladaptive emotion regulation strategies were associated with risk aversion. CONCLUSION: Childhood trauma and specific strategies of emotion regulation contribute to risk or loss aversion in patients with depression. These findings may provide useful insight into elaborative evaluation and interventions to improve decision-making and quality of life in patients with depression.

Bursts and heavy tails in temporal and sequential dynamics of foraging decisions.

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.

Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation.

The accurate diagnosis of glioma subtypes is critical for appropriate treatment, but conventional histopathologic diagnosis often exhibits significant intra-observer variability and sampling error. The aim of this study was to investigate whether histogram analysis using an automatically segmented region of interest (ROI), excluding cystic or necrotic portions, could improve the differentiation between low-grade and high-grade gliomas. Thirty-two patients (nine low-grade and 23 high-grade gliomas) were included in this retrospective investigation. The outer boundaries of the entire tumors were manually drawn in each section of the contrast-enhanced T1 -weighted MR images. We excluded cystic or necrotic portions from the entire tumor volume. The histogram analyses were performed within the ROI on normalized apparent diffusion coefficient (ADC) maps. To evaluate the contribution of the proposed method to glioma grading, we compared the area under the receiver operating characteristic (ROC) curves. We found that an ROI excluding cystic or necrotic portions was more useful for glioma grading than was an entire tumor ROI. In the case of the fifth percentile values of the normalized ADC histogram, the area under the ROC curve for the tumor ROIs excluding cystic or necrotic portions was significantly higher than that for the entire tumor ROIs (p < 0.005). The automatic segmentation of a cystic or necrotic area probably improves the ability to differentiate between high- and low-grade gliomas on an ADC map.

Early diagnosis of Alzheimer's disease and Parkinson's disease associated with dementia using cerebral perfusion SPECT.

BACKGROUND: Since patterns of cognitive dysfunction in mild Parkinson's disease associated with dementia (PDD) are similar to those in mild Alzheimer's disease (AD), it is difficult to accurately differentiate between these two types of dementia in their early phases using neuropsychological tests. The purpose of the current study was to investigate differences in cerebral perfusion patterns of patients with AD and PDD at the earliest stages using single photon emission computed tomography (SPECT). METHODS: We consecutively recruited 31 patients with mild PDD, 32 patients with mild probable AD and 33 age-matched healthy subjects. All subjects underwent (99m)Tc-hexamethylpropyleneamine oxime perfusion SPECT and completed general neuropsychological tests. RESULTS: We found that both mild PDD and AD patients showed distinct hypoperfusion in frontal, parietal and temporal regions, compared with healthy subjects. More importantly, hypoperfusion in occipital and cerebellar regions was observed only in mild PDD. CONCLUSION: The observation of a significant decrease in cerebral perfusion in occipital and cerebellar regions in patients with mild PDD is likely useful to differentiate between PDD and AD at the earliest stages.

Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence.

Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.

Auditory imagery modulates frequency-specific areas in the human auditory cortex.

Neural responses in early sensory areas are influenced by top-down processing. In the visual system, early visual areas have been shown to actively participate in top-down processing based on their topographical properties. Although it has been suggested that the auditory cortex is involved in top-down control, functional evidence of topographic modulation is still lacking. Here, we show that mental auditory imagery for familiar melodies induces significant activation in the frequency-responsive areas of the primary auditory cortex (PAC). This activation is related to the characteristics of the imagery: when subjects were asked to imagine high-frequency melodies, we observed increased activation in the high- versus low-frequency response area; when the subjects were asked to imagine low-frequency melodies, the opposite was observed. Furthermore, we found that A1 is more closely related to the observed frequency-related modulation than R in tonotopic subfields of the PAC. Our findings suggest that top-down processing in the auditory cortex relies on a mechanism similar to that used in the perception of external auditory stimuli, which is comparable to early visual systems.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Neural representations for multi-context visuomotor adaptation and the impact of common representation on multi-task performance: a multivariate decoding approach.

The human brain's remarkable motor adaptability stems from the formation of context representations and the use of a common context representation (e.g., an invariant task structure across task contexts) derived from structural learning. However, direct evaluation of context representations and structural learning in sensorimotor tasks remains limited. This study aimed to rigorously distinguish neural representations of visual, movement, and context levels crucial for multi-context visuomotor adaptation and investigate the association between representation commonality across task contexts and adaptation performance using multivariate decoding analysis with fMRI data. Here, we focused on three distinct task contexts, two of which share a rotation structure (i.e., visuomotor rotation contexts with -90° and +90° rotations, in which the mouse cursor's movement was rotated 90 degrees counterclockwise and clockwise relative to the hand-movement direction, respectively) and the remaining one does not (i.e., mirror-reversal context where the horizontal movement of the computer mouse was inverted). This study found that visual representations (i.e., visual direction) were decoded in the occipital area, while movement representations (i.e., hand-movement direction) were decoded across various visuomotor-related regions. These findings are consistent with prior research and the widely recognized roles of those areas. Task-context representations (i.e., either -90° rotation, +90° rotation, or mirror-reversal) were also distinguishable in various brain regions. Notably, these regions largely overlapped with those encoding visual and movement representations. This overlap suggests a potential intricate dependency of encoding visual and movement directions on the context information. Moreover, we discovered that higher task performance is associated with task-context representation commonality, as evidenced by negative correlations between task performance and task-context-decoding accuracy in various brain regions, potentially supporting structural learning. Importantly, despite limited similarities between tasks (e.g., rotation and mirror-reversal contexts), such association was still observed, suggesting an efficient mechanism in the brain that extracts commonalities from different task contexts (such as visuomotor rotations or mirror-reversal) at multiple structural levels, from high-level abstractions to lower-level details. In summary, while illuminating the intricate interplay between visuomotor processing and context information, our study highlights the efficiency of learning mechanisms, thereby paving the way for future exploration of the brain's versatile motor ability.

Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods.

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons.

Topological cluster analysis reveals the systemic organization of the Caenorhabditis elegans connectome.

The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors.

Decreased cortical complexity in methamphetamine abusers.

This study aimed to investigate if methamphetamine (MA) abusers exhibit alterations in complexity of the electroencephalogram (EEG) and to determine if these possible alterations are associated with their abuse patterns. EEGs were recorded from 48 former MA-dependent males and 20 age- and sex-matched healthy subjects. Approximate Entropy (ApEn), an information-theoretical measure of irregularity, of the EEGs was estimated to quantify the degree of cortical complexity. The ApEn values in MA abusers were significantly lower than those of healthy subjects in most of the cortical regions, indicating decreased cortical complexity of MA abusers, which may be associated with impairment in specialization and integration of cortical activities owing to MA abuse. Moreover, ApEn values exhibited significant correlations with the clinical factors including abuse patterns, symptoms of psychoses, and their concurrent drinking and smoking habits. These findings provide insights into abnormal information processing in MA abusers and suggest that ApEn of EEG recordings may be used as a potential supplementary tool for quantitative diagnosis of MA abuse. This is the first investigation to assess the "severity-dependent dynamical complexity" of EEG patterns in former MA abusers and their associations with the subjects' abuse patterns and other clinical measures.

Disturbances of motivational balance in chronic schizophrenia during decision-making tasks.

AIM: The role of feedback processing in decision-making has been assessed in psychiatric patients using the Iowa Gambling Task (IGT). Although impaired performance on the IGT has been documented extensively in schizophrenia patients, the neuropsychological mechanisms underlying the performance deficits have not yet been elucidated. Therefore, the aim of this study was to investigate the neuropsychological origins of impaired decision-making in schizophrenia patients using various versions of the IGT. METHODS: Thirty chronic schizophrenia patients and 33 healthy subjects underwent computerized versions of the IGT, the Variant Gambling Task (VGT), and the Shuffled Gambling Task (SGT) to assess the contributions of motivational balance and reversal learning on IGT performance. In addition, performance on the Wisconsin Card-Sorting Test (WCST) was assessed. RESULTS: The schizophrenia patients exhibited deficits on the IGT and SGT, particularly in later trials. No significant group difference was detected on the VGT due to the improved performance of schizophrenia patients in the earlier trials. Performance on the gambling tasks in the schizophrenia group did not correlate with performance on the WCST or with the severity of clinical symptoms. CONCLUSION: Deficits in motivational balance, but not reversal learning, play a dominant role in the impaired decision-making of patients with schizophrenia.

Hemispheric asymmetry in non-linear interdependence of EEG in post-traumatic stress disorder.

AIM: While volumetric and metabolic imaging on post-traumatic stress disorder (PTSD) patients has been intensively performed, few studies using electroencephalograms (EEG) have been done as yet. The aim of the present study was to investigate abnormalities in functional connectivity of cortical networks in PTSD. METHODS: Non-linear interdependence (NI), a measure of bidirectional, non-linear information transmission between two time series, was used. Resting EEG were recorded for 18 PTSD patients and 18 sex-matched healthy subjects on 16 channels with their eyes closed. RESULTS: The NI patterns in PTSD patients were hemisphere asymmetric: an increase in NI in the fronto-parieto-temporal regions of the left hemisphere (F7, F3, T3, C3, T5 and P3) and a decrease in the fronto-parieto-occipital regions of the right hemisphere (F4, C4, P4 and O2). The non-linearity of NI in EEG, estimated from the surrogate data method, exhibited an increase in the PTSD patients as compared with that of healthy subjects, particularly in the left hemispheric cortex. CONCLUSION: Abnormal functional connectivity in PTSD can be assessed using NI, a measure of multi-channel EEG.