Critical Brain Regions Research Articles

Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study.

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs—including 1 that was specific and 16 that were general—based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

Grey matter microstructural alterations in schizophrenia patients with treatment-resistant auditory verbal hallucinations

Treatment-resistant auditory verbal hallucinations (TRAVH) are a relatively prevalent and devastating symptom in patients with schizophrenia (SCZ). Even though their pathological mechanisms are poorly understood, they seem to differ from those underlying non-hallucinating SCZ.In this study, we characterise structural brain changes in SCZ patients with TRAVH. With respect to non-hallucinating patients and healthy controls, we studied macrostructural grey matter changes through cortical thickness and subcortical volumetric data. Additionally, we analysed microstructural differences across groups using intracortical and subcortical mean diffusivity data. This latter imaging metric has been claimed to detect incipient neuronal damage, as water can diffuse more freely in regions with reduced neural density.We found brain macrostructrural and microstructural alterations in SCZ patients with TRAVH (n = 29), both with respect to non-hallucinating (n = 20) patients and healthy controls (n = 27). Importantly, a microstructural –rather than a macrostructural– compromise was found in key brain regions such as the ventral ACC, the NAcc and the hippocampus. These microstructural alterations correlated, in turn, with clinical severity. TRAVH patients also showed accentuated age-related cortical deterioration and an abnormal longitudinal loss of cortical integrity over a one-year period.These findings highlight the potential role of microstructural imaging biomarkers in SCZ. Notably, they could be used both to detect and to monitor subtle grey matter alterations in critical brain regions such as deep brain stimulation targets. Moreover, our results support the existence of a more aggressive and active pathological mechanism in patients with TRAVH, providing new insight into the aetiology of this debilitating illness.

Improvements on spatial coverage and focality of deep brain stimulation in pre-surgical epilepsy mapping

Objective. Electrical stimulation mapping (ESM) of the brain using stereo-electroencephalography (SEEG) intracranial electrodes, also known as depth-ESM (DESM), is being used as part of the pre-surgical planning for brain surgery in drug-resistant epilepsy patients. Typically, DESM consists in applying the electrical stimulation using adjacent contacts of the SEEG electrodes and in recording the EEG responses to those stimuli, giving valuable information of critical brain regions to better delimit the region to resect. However, the spatial extension or coverage of the stimulated area is not well defined even though the precise electrode locations can be determined from computed tomography images. Approach. We first conduct electrical simulations of DESM for different shapes of commercial SEEG electrodes showing the stimulation extensions for different intensities of injected current. We then evaluate the performance of DESM in terms of spatial coverage and focality on two realistic head models of real patients undergoing pre-surgical evaluation. We propose a novel strategy for DESM that consist in applying the current using contacts of different SEEG electrodes (x-DESM), increasing the versatility of DESM without implanting more electrodes. We also present a clinical case where x-DESM replicated the full semiology of an epilepsy seizure using a very low-intensity current injection, when typical adjacent DESM only reproduced partial symptoms with much larger intensities. Finally, we show one example of DESM optimal stimulation to achieve maximum intensity, maximum focality or intermediate solution at a pre-defined target, and one example of temporal interference in DESM capable of increasing focality in brain regions not immediately touching the electrode contacts. Main results. It is possible to define novel current injection patterns using contacts of different electrodes (x-DESM) that might improve coverage and/or focality, depending on the characteristics of the candidate brain. If individual simulations are not possible, we provide the estimated radius of stimulation as a function of the injected current and SEEG electrode brand as a reference for the community. Significance. Our results show that subject-specific electrical stimulations are a valuable tool to use in the pre-surgical planning to visualize the extension of the stimulated regions. The methods we present here are also applicable to pre-surgical planning of tumor resections and deep brain stimulation treatments.

Transcranial direct current stimulation of the right dorsolateral prefrontal cortex improves response inhibition

BackgroundA number of functional magnetic resonance imaging studies have shown that the dorsolateral prefrontal cortex (dlPFC) is a critical brain region for response inhibition. However, how it exerts this function remains unclear. This study investigated whether stimulating the right dlPFC by transcranial direct current stimulation (tDCS) affects performance on stop signal task. MethodsA total of 92 healthy subjects were enrolled in the study and randomly divided into three groups. The anode group received anodal stimulation over the right dlPFC and cathodal stimulation over the left supraorbital; the cathode group received cathodal stimulation over the right dlPFC and anodal stimulation over the left supraorbital; and the sham group received sham tDCS. All subjects performed a computer-based stop-signal task before and after tDCS. ResultPerformance on the response inhibition task after tDCS was improved in groups with both anodal and cathodal stimulation. Specifically, there was a decrease in the stop-signal reaction time in these subjects, whereas no difference was observed in the sham group. Consistent with signal detection theory, discrimination and decision bias was improved by anode tDCS relative to the sham group, while discrimination was also improved in the cathode group. ConclusionAnode and cathode tDCS of the right dlPFC improves response inhibition, with the right dlPFC may playing a key role in this process.

Hyperconnection and hyperperfusion of overlapping brain regions in patients with menstrual-related migraine: a multimodal neuroimaging study.

Menstrual-related migraine (MRM) results in moderate to severe intensity headaches accompanied by physical and emotional disability over time in women. Neuroimaging methodologies have advanced our understanding of migraine; however, the neural mechanisms of MRM are not clearly understood. In this study, fourteen MRM patients in the interictal phase and fifteen age- and education-matched healthy control females were recruited. Resting-state functional magnetic resonance imaging (fMRI) and pulsed arterial spin labeling (PASL) MRI were collected for both the subject groups outside of their menstrual periods. Eigenvector centrality mapping (ECM) was performed on resting-state fMRI, and the relative cerebral blood flow (relCBF) was assessed using PASL-MRI. MRM patients showed a significantly increased eigenvector centrality in the right medial frontal gyrus compared to healthy controls. Seed-based ECM analysis revealed that increased centrality was associated with the right medial frontal gyrus's hyperconnectivity with the left insula and the right supplementary motor area. The perfusion MRI revealed significantly increased relCBF in the hyperconnected regions. Furthermore, the hyperconnection positively correlated with the attack frequency, while the hyperperfusion showed a positive correlation with the disease duration. The results suggest that menstrual-related migraine is associated with cerebral hyperconnection and hyperperfusion in critical pain-processing brain regions. Furthermore, this elevated cerebral activity is correlated with different aspects of functional impairment in MRM patients suggesting that perfusion analysis, along with whole-brain connectivity analysis, can provide a comprehensive understanding of neural mechanisms of MRM.

Brain network analyses of diffusion tensor imaging for brain aging.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection

Electroencephalography (EEG) signals have been proven to be one of the most predictive and reliable indicators for estimating driving fatigue state. However, how to make full use of EEG data for driving fatigue detection remains a challenge. Many existing methods include a time-consuming manual process or tedious parameter tunings for feature extraction, which is inconvenient to train and implement. On the other hand, most models ignore or manually determine EEG connectivity features between different channels, thus failing to thoroughly exploit the intrinsic interchannel relations for classification. In this article, we introduce a new attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN) model, aiming to conquer these two issues in a unified end-to-end model. AMCNN-DGCN starts with attention-based multiscale temporal convolutions to automatically learn frequency filters to extract the salient pattern from raw EEG data. Subsequently, AMCNN-DGCN uses dynamical graph convolutional networks (DGCNs) to learn spatial filters, in which the adjacency matrix is adaptively determined in a data-driven way to exploit the intrinsic relationship between channels effectively. With the temporal–spatial structure, AMCNN-DGCN can capture highly discriminative features. To verify the effectiveness of AMCNN-DGCN, we conduct a simulated fatigue driving environment to collect EEG signals from 29 healthy subjects (male/female = 17/12 and age = 23.28±2.70 years) through a remote wireless cap with 24 channels. The results demonstrate that our proposed model outperforms six widely used competitive EEG models with high accuracy of 95.65%. Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix.

IL-10 normalizes aberrant amygdala GABA transmission and reverses anxiety-like behavior and dependence-induced escalation of alcohol intake.

Alcohol elicits a neuroimmune response in the brain contributing to the development and maintenance of alcohol use disorder (AUD). While pro-inflammatory mediators initiate and drive the neuroimmune response, anti-inflammatory mediators provide an important homeostatic mechanism to limit inflammation and prevent pathological damage. However, our understanding of the role of anti-inflammatory signaling on neuronal physiology in critical addiction-related brain regions and pathological alcohol-dependence induced behaviors is limited, precluding our ability to identify promising therapeutic targets. Here, we hypothesized that chronic alcohol exposure compromises anti-inflammatory signaling in the central amygdala, a brain region implicated in anxiety and addiction, consequently perpetuating a pro-inflammatory state driving aberrant neuronal activity underlying pathological behaviors. We found that alcohol dependence alters the global brain immune landscape increasing IL-10 producing microglia and T-regulatory cells but decreasing local amygdala IL-10 levels. Amygdala IL-10 overexpression decreases anxiety-like behaviors, suggesting its local role in regulating amygdala-mediated behaviors. Mechanistically, amygdala IL-10 signaling through PI3K and p38 MAPK modulates GABA transmission directly at presynaptic terminals and indirectly through alterations in spontaneous firing. Alcohol dependence-induces neuroadaptations in IL-10 signaling leading to an overall IL-10-induced decrease in GABA transmission, which normalizes dependence-induced elevated amygdala GABA transmission. Notably, amygdala IL-10 overexpression abolishes escalation of alcohol intake, a diagnostic criterion of AUD, in dependent mice. This highlights the importance of amygdala IL-10 signaling in modulating neuronal activity and underlying anxiety-like behavior and aberrant alcohol intake, providing a new framework for therapeutic intervention.

Tracking Cortical Changes Throughout Cognitive Decline in Parkinson's Disease.

The objectives of this study were to investigate progressive cortical thinning and volume loss in Parkinson's disease (PD) patients with different longitudinal patterns of cognitive decline: with stable normal cognition, with stable mild cognitive impairment, with conversion to mild cognitive impairment, and with conversion to dementia. We recruited 112 patients (37 Parkinson's disease with stable normal cognition, 20 Parkinson's disease with stable mild cognitive impairment, 36 Parkinson's disease with conversion to mild cognitive impairment, 19 Parkinson's disease with conversion to dementia) and 38 healthy controls. All patients underwent at least 2 visits within 4 years including clinical/cognitive assessments and structural MRI (total visits, 393). Baseline cortical thickness and gray matter volumetry were compared between groups. In PD, gray matter changes over time were investigated and compared between groups. At baseline, compared with Parkinson's disease with stable normal cognition cases, Parkinson's disease with conversion to mild cognitive impairment patients showed cortical atrophy of the parietal and occipital lobes, similar to Parkinson's disease with stable mild cognitive impairment and Parkinson's disease with conversion to dementia patients. The latter groups (ie, patients with cognitive impairment from the study entry) showed additional involvement of the frontotemporal cortices. No baseline volumetric differences among groups were detected. The longitudinal analysis (group-by-time interaction) showed that, versus the other patient groups, Parkinson's disease with stable mild cognitive impairment and Parkinson's disease with conversion to dementia cases accumulated the least cortical damage, with Parkinson's disease with conversion to dementia showing unique progression of right thalamic and hippocampal volume loss; Parkinson's disease with conversion to mild cognitive impairment patients showing specific cortical thinning accumulation in the medial and superior frontal gyri, inferior temporal, precuneus, posterior cingulum, and supramarginal gyri bilaterally; and Parkinson's disease with stable normal cognition patients showing cortical thinning progression, mainly in the occipital and parietal regions bilaterally. Cortical thinning progression is more prominent in the initial stages of PD cognitive decline. The involvement of frontotemporoparietal regions, the hippocampus, and the thalamus is associated with conversion to a more severe stage of cognitive impairment. In PD, gray matter alterations of critical brain regions may be an MRI signature for the identification of patients at risk of developing dementia. © 2020 International Parkinson and Movement Disorder Society.

Anterior Insular Resting-State Functional Connectivity is Related to Cognitive Reserve in Multiple Sclerosis.

Cognitive dysfunction is common in multiple sclerosis (MS). The dorsal anterior insula (dAI) is a key hub of the salience network (SN) orchestrating access to critical cognitive brain regions. The aim of this study was to assess whole-brain dAI intrinsic functional connectivity (iFC) using resting-state functional MRI (rs-fMRI) in people with MS and healthy controls (HC) and test the relationship between cognitive reserve (CR) and dAI iFC in people with MS. We studied 28 people with relapsing-remitting MS and 28 HC. CR index was quantified by combining premorbid IQ, leisure activities, and education level. For whole-brain iFC analyses, the bilateral dAI were used as seeds. Individual subject correlation maps were entered into general linear models for group comparison and to analyze the effect of CR index on dAI iFC, controlling for multiple comparisons. The correlation between CR index and iFC was assessed using a linear regression model. rs-fMRI analyses revealed a negative relationship between CR index and iFC within the left dAI and a left occipital cluster in people with MS including regions of the cuneus, superior occipital gyrus, and parieto-occipital sulcus. The regression analysis showed that people with MS and a higher CR index had a statistically significantly reduced iFC within the left dAI and the cluster. CR is relevant to functional connectivity within one of the main nodes of the SN, the dAI, and occipital regions in MS. These results have implications for how CR may modulate the susceptibility to cognitive dysfunction in MS.

Genetic predictors of hippocampal subfield volume in PTSD cases and trauma-exposed controls

ABSTRACT Behavioural, structural, and functional neuroimaging have implicated the hippocampus as a critical brain region in posttraumatic stress disorder (PTSD) pathogenesis. Recent work in a normative, primarily European, sample identified 15 unique genetic loci contributing to structural variability in six hippocampal subfield volumes. We explored the relevance of these loci in two samples (Mental Illness Research Education and Clinical Centre [MIRECC] and Grady; n = 290) of trauma-exposed individuals enriched for PTSD and of diverse ancestry. Four of the previous loci demonstrated nominal evidence of replication in the MIRECC dataset, primarily within non-Hispanic whites (NHW). One locus replicated in the Grady cohort, which was composed exclusively of non-Hispanic blacks (NHB). Our data supported genetic interactions with diagnosis of lifetime PTSD and genetic interactions with childhood trauma in the MIRECC sample, but not the Grady sample. Given the racial, diagnostic, and trauma-exposure differences with the original genome-wide association study (GWAS) report, we conducted a full GWAS in the MIRECC and Grady datasets. Interactions between genetic variants and lifetime PTSD or childhood trauma were interrogated for single nucleotide polymorphisms (SNPs) with evidence of main effects. Genetic associations surpassed false discovery rate (FDR)-correction within hippocampal subfields in fimbria, subiculum, cornu ammonis-1 (CA1), and hippocampal amygdala transition area (HATA). One association was replicated in the Grady cohort (rs12880795 in TUNAR with left (L)-HATA volume). The most significant association in the MIRECC dataset was between rs6906714 in LINC02571 and right (R)-fimbria volume (p = 5.99×10−8, q = 0.0056). Interestingly, the effect of rs6906714 on R-fimbria volume increased with exposure to childhood trauma (gene*environment [G*E] interaction p = 0.022). These preliminary results argue for G*E interactions between genetic loci with PTSD and childhood trauma on hippocampal phenotypes. Our results underscore the need for larger neuroimaging-genetic studies in PTSD, trauma, and ancestrally diverse populations.

Decoding Hearing-Related Changes in Older Adults' Spatiotemporal Neural Processing of Speech Using Machine Learning.

Speech perception in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of cortical speech-evoked responses that distinguish older adults with or without mild hearing loss. We performed source analyses to estimate cortical surface signals from the EEG recordings during a phoneme discrimination task conducted under clear and noise-degraded conditions. We computed source-level ERPs (i.e., mean activation within each ROI) from each of the 68 ROIs of the Desikan-Killiany (DK) atlas, averaged over a randomly chosen 100 trials without replacement to form feature vectors. We adopted a multivariate feature selection method called stability selection and control to choose features that are consistent over a range of model parameters. We use parameter optimized support vector machine (SVM) as a classifiers to investigate the time course and brain regions that segregate groups and speech clarity. For clear speech perception, whole-brain data revealed a classification accuracy of 81.50% [area under the curve (AUC) 80.73%; F1-score 82.00%], distinguishing groups within ∼60 ms after speech onset (i.e., as early as the P1 wave). We observed lower accuracy of 78.12% [AUC 77.64%; F1-score 78.00%] and delayed classification performance when speech was embedded in noise, with group segregation at 80 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured in the RH. Moreover, stability selection analysis identified 12 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 16 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (78.7% accuracy). Our results identify critical time-courses and brain regions that distinguish mild hearing loss from normal hearing in older adults and confirm a larger number of active areas, particularly in RH, when processing noise-degraded speech information.

Dynamic Functional Connectivity Fading Analysis and Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Normal Control Subjects based on Resting-State fMRI Data

In this paper, motivated by the fading effect in wireless communications, where severe channel fading is related to information loss during the transmission, we evaluate and analyze the fading effect in time-varying functional connectivity of AD, MCI and NC subjects based on the resting-state fMRI data, and then apply that for AD, MCI, NC classification. We show that in some critical brain regions, compared with NC subjects, AD subjects suffer more severe and long lasting fading in the functional connectivity level; in other words, AD subjects show selective loss in the amount of information successfully exchanged between the brain regions. On the other hand, MCI subjects experience less severe and shorter fading in functional connectivity level in general, and the connectivity level of MCI may be tangled together with that of either NC or AD. The underlying neurobiological basis for the possible information loss during the transmission process in AD is that the most vulnerable neurons in AD are the association neurons with long projections that formulate the communication channels or links between the brain regions, and these vulnerable neurons often suffer from loss of dendrites that leads to a significant impairment of synaptic transmission. We also show that, compared with static network connectivity pattern analysis that extracts only the region-level spatial variability, dynamic network connectivity pattern analysis, which exploits both the temporal and spatial variability in functional connectivity, can achieve much higher accuracy in the classification of AD, MCI and NC. When the AD, MCI and NC subjects are all mixed together, the prediction accuracy of time-varying connectivity based classification is 90.9%, 75.0% and 80.0% for NC, MCI and AD, respectively. Our result is consistent with existing results on dynamic functional connectivity analysis for AD and MCI.

Angiotensin (1–7) through modulation of the NMDAR–nNOS–NO pathway and serotonergic metabolism exerts an anxiolytic-like effect in rats

Although recent studies have shown that angiotensin (1–7) (Ang [1–7]) exerts anti-stress and anxiolytic-like effects, the underlying mechanisms remain elusive. The ventral hippocampus (VH) is proposed to be a critical brain region for mood and stress management through the N-methyl-d-aspartate receptor (NMDAR) signaling pathway. However, the role of VH NMDAR signaling in the effects of Ang (1–7) remains unclear. In the present study, Ang (1–7) was injected into the bilateral VH of stressed rats, or in combination with a Fyn kinase inhibitor, NMDAR antagonist, neuronal nitric oxide synthase (nNOS) inhibitor, or nitric oxide (NO) scavenger. Anxiety-like behaviors were assessed using the open field test and elevated plus maze test, while alterations in NMDAR–nNOS–NO signaling and serotonergic metabolism were examined in the VH. After 21 days of chronic restraint stress, anxiety-like behaviors were evident. Levels of phosphorylated NR2B (a key NMDAR subunit), its upstream kinase Fyn, as well as activity of nNOS and monoamine oxidase (MAO) were markedly reduced. In contrast, levels of serotonin were increased. Bilateral VH infusion of Ang (1–7) recovered NMDAR–nNOS–NO signaling and MAO-mediated serotonin metabolism, as well as reducing anxiety-like behaviors in stressed rats. These effects were diminished by blockade of MasR (Ang [1–7]-specific receptor), Fyn kinase, NMDAR, nNOS, or NO production. Altogether, these findings indicate that Ang (1–7) exerts anxiolytic effects through modulation of the NMDAR–nNOS–NO pathway and serotonergic metabolism. Future translational research should focus on the relationship between Ang (1–7), glutamatergic neurotransmission, and serotonergic neurotransmission in the VH.

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery.

The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

Infralimbic cortex activity is required for the expression but not the acquisition of conditioned safety

The ability to discriminate between danger and safety is crucial for survival across species. Whereas danger signals predict the onset of a potentially threatening event, safety signals indicate the non-occurrence of an aversive event, thereby reducing fear and stress responses. While the neural basis of conditioned safety remains to be elucidated, fear extinction studies provide evidence that the infralimbic cortex (IL) modulates fear inhibition. In the current study, the IL was temporarily inactivated with local muscimol injections in male and female rats. The effect of IL inactivation on the acquisition and expression of conditioned safety was investigated utilizing the startle response. Temporary inactivation of the IL prior to conditioning did not affect the acquisition of conditioned safety, whereas IL inactivation during the expression test completely blocked the expression of conditioned safety in male and female rats. Inactivation of the neighboring prelimbic (PL) cortex during the expression test did not affect the expression of safety memory. Our findings suggest that the IL is a critical brain region for the expression of safety memory. Because patients suffering from anxiety disorders are often unable to make use of safety cues to inhibit fear, the present findings are of clinical relevance and could potentially contribute to therapy optimization of anxiety-related psychiatric disorders.

Primary Cognitive Factors Impaired after Glioma Surgery and Associated Brain Regions.

Previous studies have shown that cognitive impairments in patients with brain tumors are not severe. However, to preserve the postsurgical QOL of patients with brain tumors, it is important to identify “primary” cognitive functions and associated brain regions that are more vulnerable to cognitive impairments following surgery. The objective of this study was to investigate primary cognitive factors affecting not only simple cognitive tasks but also several other cognitive tasks and associated brain regions. Patients with glioma in the left (n = 33) and the right (n = 21) hemisphere participated in the study. Seven neuropsychological tasks from five cognitive domains were conducted pre- and 6 months postoperation. Factor analyses were conducted to identify “primary” common cognitive functions affecting the task performance in left and right glioma groups. Next, lesion analyses were performed using voxel-based lesion-symptom mapping (VLSM) to identify critical brain regions related to impairments of the primary cognitive functions. Factor analysis revealed two primary cognitive components in each glioma group. The first cognitive component in the left glioma group affected the digit span forward and backward tasks and concept shifting and the letter-digit substitution tasks. VLSM analysis revealed significant regions from the posterior middle temporal gyri to the supramarginal gyrus. The second cognitive component affected verbal memory, and verbal fluency tasks and VLSM analysis indicated two different significant regions, the medial temporal regions and the middle temporal gyrus to the posterior parietal lobes. The first cognitive component in the right glioma group affected positive and negative factor loadings on the task, such that the positive cognitive component affected only the Stroop color-word task. VLSM related to deficits of the Stroop task revealed significant regions in the anterior medial frontal cortex. On the other hand, the negative component affected concept shifting, word fluency, and digit span forward tasks, and VLSM revealed significant regions in the right inferior frontal cortex. It is suggested that primary cognitive functions related to specific brain regions were possibly affected by glioma resection.

Administration of moclobemide facilitates fear extinction and attenuates anxiety-like behaviors by regulating synaptic-associated proteins in a rat model of post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is a long-lasting mental disorder and accompanied by worse fear extinction. Enhanced fear memory or poor fear extinction are typical features of PTSD. Dysfunction of the serotonergic neurotransmitter system is involved in numerous mental and behavioral disorders. Monoamine oxidase A (MAOA) is important in the metabolism of serotonin and play an important role in behavious. The aim of this study was to explore the change of MAOA and effect of MAOA on fear memory in PTSD. We used single prolonged stress (SPS) to create animal model of PTSD. A startle/fear box and elevated plus maze were used to observe fear memory and anxiety level, respectively. We examined the expression of MAOA and synaptic marker protein, as well as the immunological activity of MAOA in the infralimbic cortex (IL) area, which is a critical brain region involved in emotions, especially fear regulation. We found increased anxiety-like behavior, dysfunction in fear extinction, and increased MAOA in SPS rats. After treatment with moclobemide (a selective inhibitor of MAOA), SPS rats showed significantly improved fear memory and decreased anxiety-like behavior, which indicated that moclobemide could reverse fear extinction deficit and attenuate abnormally increased levels of anxiety caused by SPS in short term. On the contrary, decreased PSD-95 and SYN1 expression in the IL region were also reversed by moclobemide. These results suggest that increased MAOA play a negative role in fear extinction and levels of anxiety in PTSD, which may be involved in change in PSD-95 and SYN1.

Abstract WP64: Perfusion Imaging Can Identify Basilar Occlusion Patients With a Favorable Response to Thrombectomy

Introduction: Perfusion imaging can identify patients who respond favorably to endovascular therapy (EVT) in the anterior circulation; no data are available for the posterior circulation. We evaluated perfusion patterns, assessed with RAPID software, in a consecutive cohort of patients with basilar artery occlusion treated with EVT and assessed the association between reperfusion and favorable clinical outcome based on the perfusion profile. Hypothesis: We hypothesized that patients with limited regions of severe hypoperfusion (Tmax &gt; 10s) would have a favorable response (mRS 0-2) to reperfusion (mTICI 2b-3) while patients with multiple critical brain regions severely hypoperfused would have poor outcome (mRS 4-6) despite reperfusion. Methods: From a multicenter cohort of perfusion imaging in posterior circulation stroke, we included patients with basilar artery occlusion and EVT. We pre-specified a Critical Area Perfusion Score (CAPS, 0 - 8 points) to identify severe hypoperfusion (Tmax &gt;10s) in the following regions: inferior and/or superior cerebellar hemisphere (1-4 points), pons (2 points), midbrain/thalamus (2 points). We compared the outcome between reperfusers and non reperfusers based on the CAP score with univariate and multivariate analysis. Results: 38 patients met the inclusion criteria. Mean age was 63±17, 34% female, NIHSS 17±11. In patients who reperfused (n=30, 79%) 63% had favorable outcome, while no patient without reperfusion survived, p=0.003 OR=29 (95%CI 1.5-547). Ninety percent (19/21) of reperfused patients with CAPS ≤2 had a favorable outcome, while none of the 9 with reperfusion and a score &gt;2 survived, p&lt;0.001, OR=148 (95%CI 6.5-3,333). In univariate analysis, favorable outcome was associated with NIHSS OR=0.87 (95% CI 0.80-0.96), p=0.003, and mismatch volume OR=0.98 (95% CI 0.97-0.997) p=0.013. In the multivariate analysis, only CAPS was an independent predictor of favorable outcome. Conclusions: Patients with limited regions of severe hypoperfusion (Tmax &gt; 10s) had a robust response to basilar artery EVT, however, all patients with multiple critical brain regions severely hypoperfused died despite successful reperfusion. Perfusion imaging profiles may help identify optimal patients for basilar EVT.

Improving Orthographic Awareness and Reading Fluency in Chinese Children with Dyslexia: A Case Study

To develop and evaluate a comprehensive intervention combining transcranial direct current stimulation (tDCS) and behavioral training that specifically targets orthographic awareness and reading fluency for in Chinese children with dyslexia, this study selected two Chinese children with different subtypes of dyslexia, i.e., the orthographic deficit subtype and the global deficit subtype. We utilized a case study design to delineate detailed performances of the two children throughout the whole training process. Before and after the intervention, functional magnetic resonance imaging (fMRI) technology was deployed to measure the level of brain activation in a visual phonological identification task. In addition, the children’s related reading skills were assessed through standardized tests before and after each intervention stage to evaluate changes in their reading ability. The results indicated that (1) etymological literacy teaching was not very effective in improving the orthographic awareness and reading fluency of the dyslexic child with an orthographic deficit, but it significantly improved these skills in the dyslexic child with global deficits. (2) The combination of etymological literacy teaching and tDCS not only improved orthographic awareness and reading fluency in the two dyslexic children but also enhanced the activation levels of critical brain regions associated with reading. In sum, the combination of literacy teaching and tDCS demonstrated effectiveness in improving reading skills of the two children with dyslexia of different subtypes, which could provide positive reference and guidance for intervention practices for children with dyslexia and classroom teaching in Chinese primary schools.