Individual Brain Regions Research Articles

Aerobic Exercise Training Effects on NrF2 and the Antioxidant Defense System

Nuclear factor erythroid 2 related factor 2 (NrF2), is an essential transcription factor and master regulator of the antioxidant defense system aiding in cellular protection and survival. PURPOSE: To determine the effect of chronic aerobic exercise on NrF2 and antioxidant factors in individual brain regions. METHODS: Male Sprague Dawley rats (n=12-13/group), 6 weeks of age, were exercise trained (ET) or were sedentary controls (SD). The exercised rats ran on a treadmill using a ramped protocol for 5-7 weeks at an intensity equal to ~75% VO2max. Five hours after the final exercise session rats were euthanized and the cortex, hippocampus, and cerebellum brain regions were collected and stored at -80°C until further analysis. NrF2 protein concentration was measured via western blot analysis. Total glutathione (TGSH) and reduced glutathione (GSH) concentration were measured via HPLC. Manganese superoxide dismutase (Mn-SOD) activity was measured using a spectrophotometric assay. All samples were analyzed in duplicate. The significance level was set a-priori at p<0.05 and the results are displayed as the mean ± SEM. RESULTS: Hippocampal NrF2 was significantly elevated with exercise (ET=3.62±0.20 vs. SD=2.28±0.10 arbitrary units), but was significantly reduced in the cortex (ET=3.20±0.24 vs. SD=6.39±0.26 arbitrary units) and cerebellum (ET=2.02±0.11 vs. SD=3.12±0.16 arbitrary units). TGSH and GSH significantly increased in the hippocampus (ET=182.76±4.64 vs. SD=135.54±4.89 umol/mg protein) (ET=178.94±4.59 vs. SD=131.36±4.83 umol/mg protein), respectively, but were unchanged in cortex and cerebellum regions. No significant differences were detected in Mn-SOD with aerobic exercise in any brain region. CONCLUSIONS: NrF2 and antioxidant factors were up-regulated in the hippocampus only with chronic aerobic exercise training compared to sedentary controls. However, other brain regions respond differently to aerobic exercise. This merits notation as the hippocampus is a primary brain region susceptible to neurodegenerative diseases.

A Label-fusion-aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation.

The extremely low tissue contrast in white matter during an infant's isointense stage (6-8 months) of brain development presents major difficulty when segmenting brain image regions for analysis. We sought to develop a label-fusion-aided deep-learning approach for automatically segmenting isointense infant brain images into white matter, gray matter and cerebrospinal fluid using T1- and T2-weighted magnetic resonance images. A key idea of our approach is to apply the fully convolutional neural network (FCNN) to individual brain regions determined by a traditional registration-based segmentation method instead of training a single model for the whole brain. This provides more refined segmentation results by capturing more region-specific features. We show that this method outperforms traditional joint label fusion and FCNN-only methods in terms of Dice coefficients using the dataset from iSEG MICCAI Grand Challenge 2017.

Abnormal ventral tegmental area-anterior cingulate cortex connectivity in Parkinson’s disease with depression

Neuropathology suggests that Parkinson’s disease (PD) with depression may involve a progressive degeneration of the nigrostriatal and mesocorticolimbic dopaminergic systems. Previous positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies have shown that dopamine changes in individual brain regions constituting the nigrostriatal and mesocorticolimbic circuits are associated with depression in PD. However, few studies have been conducted on the circuit-level alterations in this disease. The present study used resting-state fMRI and seed-based functional connectivity of putative dopaminergic midbrain regions (i.e., substantia nigra (SN) and ventral tegmental area (VTA)) to investigate the circuit-related abnormalities in PD with depression. The results showed that depressed PD (DPD) patients relative to healthy controls (HC) and non-depressed PD (NDPD) patients had increased functional connectivity between VTA and anterior cingulate cortex (ACC), demonstrating that dysfunctional mesocorticolimbic dopaminergic neurotransmission may be associated with depression in PD. Compared with HC, DPD and NDPD patients showed increased functional connectivity from SN to sensorimotor cortex, validating that alterations in the nigrostriatal circuitry could be responsible for cardinal motor features in PD. In addition, aberrant connectivity between VTA and ACC was correlated with the severity of depression in PD patients, further supporting that abnormal mesocorticolimbic system may account for depressive symptoms in PD. These results have provided potential circuit-level biomarkers of depression in PD, and suggested that resting state functional connectivity of midbrain dopaminergic nuclei may be useful for understanding the underlying pathology in PD with depression.

Dynamic Flexibility in Striatal-Cortical Circuits Supports Reinforcement Learning.

Complex learned behaviors must involve the integrated action of distributed brain circuits. Although the contributions of individual regions to learning have been extensively investigated, much less is known about how distributed brain networks orchestrate their activity over the course of learning. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning in humans. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex (n = 22; 13 females). Moreover, we found that this flexibility in striatal network coupling correlates with participants' learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that episodic learning, measured separately in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.SIGNIFICANCE STATEMENT Learning from the outcomes of actions, referred to as reinforcement learning, is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of updating values for actions or cues. Missing from this account, however, is an understanding of how different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

Evidence for verbal memory enhancement with electrical brain stimulation in the lateral temporal cortex.

Direct electrical stimulation of the human brain can elicit sensory and motor perceptions as well as recall of memories. Stimulating higher order association areas of the lateral temporal cortex in particular was reported to activate visual and auditory memory representations of past experiences (Penfield and Perot, 1963). We hypothesized that this effect could be used to modulate memory processing. Recent attempts at memory enhancement in the human brain have been focused on the hippocampus and other mesial temporal lobe structures, with a few reports of memory improvement in small studies of individual brain regions. Here, we investigated the effect of stimulation in four brain regions known to support declarative memory: hippocampus, parahippocampal neocortex, prefrontal cortex and temporal cortex. Intracranial electrode recordings with stimulation were used to assess verbal memory performance in a group of 22 patients (nine males). We show enhanced performance with electrical stimulation in the lateral temporal cortex (paired t-test, P = 0.0067), but not in the other brain regions tested. This selective enhancement was observed both on the group level, and for two of the four individual subjects stimulated in the temporal cortex. This study shows that electrical stimulation in specific brain areas can enhance verbal memory performance in humans.awx373media15704855796001.

Measurement of Mitochondrial Toxicity Parameters in Embryonic Hippocampus.

Recent discoveries have focused on mitochondria functions in the neuroscience research for approaches to study mitochondria dysfunction in neurodegenerative diseases. Mitochondrion is one of the organelles that is possibly worst affected in cognitive impairments. These are known as "powerhouse" of the cell as they are the main source of generation of ATP through aerobic respiration. They have role in oxidative phosphorylation and metabolism, they play central role in cell differentiation, apoptosis, oxygen sensing and detoxification of reactive oxygen species, innate immunity, mitochondrial matrix calcium, and maintenance of cell quality and regulation of cytoplasmic. There is a relationship between mitochondrial dysfunction and cognitive disorder that may be related to certain neurotoxins or mutations in mitochondrial DNA as well as the nuclear. Evaluating compounds for mitochondrial toxicity is an important capability for evaluation of cognitive effects by drugs. Studying mitochondria isolated from individual mouse brain regions is a challenge because of small amount of the available brain tissue. There are conventional techniques for isolation and purification of mitochondria from ventral midbrain, hippocampus, or striatum. The utilization of alcohol within pregnancy impairs the development of the unborn offspring and can lead to a plethora of anatomical, behavioral, and cognitive abnormalities.In here, a method for isolation of brain mitochondria from mouse is described. The method utilizes a refrigerated tabletop microtube centrifuge, and produces research grade quality mitochondria in amounts sufficient for performing multiple enzymatic and functional assays, thereby eliminating the necessity for pooling mouse brain tissue. A method for measuring ROS measurement, mitochondrial membrane potential, mitochondrial swelling, cytochrome c release, and mtDNA alterations after exposure to drugs is also included.

Deep Radiomic Analysis of MRI Related to Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive impairment of memory and cognitive functions. Radiomic features obtained from brain MRI have shown a great potential as non-invasive biomarkers for this disease; however, their usefulness has not yet been explored for individual brain regions. In this paper, we hypothesize that distinct regions are affected differently by AD and, thus, that shape or texture changes occurring in separate regions can be expressed by different radiomic features. Moreover, to improve the classification of AD and healthy control (HC) subjects, we propose novel features based on the entropy of the convolution neural network (CNN) feature maps. The proposed approach is evaluated comprehensively using the Open Access Series of Imaging Studies database. Our experiments assess the significance of 45 different radiomic features from individual subcortical regions, via the Wilcoxon test. We also use the random forest classifier to identify the subcortical regions that best differentiate AD patients from HC subjects. Our analysis identified the features derived from several subcortical regions that show significant differences between AD and HC (corrected $p ). Specifically, we found correlation and volume features from the hippocampus (AUC = 81.19% – 84.09%) and amygdala (AUC = 79.70% – 80.27%) regions to have the greatest discriminative power. Furthermore, the proposed entropy features derived from CNN layers yielded the highest classification AUC of 92.58%, compared to 84.45% for the combined radiomic features of all subcortical. These results suggest that the proposed CNN entropy features could be used as an effective biomarker for AD.

Exploring the reproducibility of functional connectivity alterations in Parkinson's disease.

Since anatomic MRI is presently not able to directly discern neuronal loss in Parkinson’s Disease (PD), studying the associated functional connectivity (FC) changes seems a promising approach toward developing non-invasive and non-radioactive neuroimaging markers for this disease. While several groups have reported such FC changes in PD, there are also significant discrepancies between studies. Investigating the reproducibility of PD-related FC changes on independent datasets is therefore of crucial importance. We acquired resting-state fMRI scans for 43 subjects (27 patients and 16 normal controls, with 2 replicate scans per subject) and compared the observed FC changes with those obtained in two independent datasets, one made available by the PPMI consortium (91 patients, 18 controls) and a second one by the group of Tao Wu (20 patients, 20 controls). Unfortunately, PD-related functional connectivity changes turned out to be non-reproducible across datasets. This could be due to disease heterogeneity, but also to technical differences. To distinguish between the two, we devised a method to directly check for disease heterogeneity using random splits of a single dataset. Since we still observe non-reproducibility in a large fraction of random splits of the same dataset, we conclude that functional heterogeneity may be a dominating factor behind the lack of reproducibility of FC alterations in different rs-fMRI studies of PD. While global PD-related functional connectivity changes were non-reproducible across datasets, we identified a few individual brain region pairs with marginally consistent FC changes across all three datasets. However, training classifiers on each one of the three datasets to discriminate PD scans from controls produced only low accuracies on the remaining two test datasets. Moreover, classifiers trained and tested on random splits of the same dataset (which are technically homogeneous) also had low test accuracies, directly substantiating disease heterogeneity.

(Invited) Novel Carbon-Based Sensors for the Next Generation of Neurochemical Measurements

Carbon-fiber microelectrodes have been the fundamental tool for the electrochemical detection of neurotransmitters since their introduction in the early 1980s. They are biologically compatible and, compared to larger electrodes, microelectrodes offer highly increased diffusional flux to the electrode surface and faster response times to enable high speed measurements. The demonstrated benefit of enhanced mass transport to very small electrodes has led us to develop several new carbon-based microelectrodes for the improved detection of dynamic neurochemical fluctuations. This talk will describe the characterization of a recessed microdisk electrode for enhanced detection of individual exocytosis events at single cells, a nanoscale microelectrode with a conical geometry for fast intracellular voltammetric measurements, and innovative yarn-like sensing substrates engineered entirely of multi-walled carbon nanotubes for enhanced sensitivity when monitoring from individual cells or discrete brain regions. Each of these ultramicroelectrode designs exploits mass transport to either provide maximal diffusional flux to, or to limit diffusional flux from, the electrode surface. These new tools provide significant advantages when coupled with fast-scan cyclic voltammetry, and applications that provide new insight into neurochemical dynamics will be discussed.

Cerebral regional oxygen saturation trends in infants with hypoxic-ischemic encephalopathy

BackgroundNeurological outcomes in neonatal hypoxic-ischemic encephalopathy (HIE) continue to be sub-optimal despite therapeutic hypothermia (TH). Cerebral near-infrared spectroscopy provides real-time regional oxygen saturation (CrSO2) that may be a marker of adverse MRI findings and neurodevelopmental outcomes. AimThe aim of this study was to examine the value of CrSO2 monitoring in infants with HIE undergoing TH. Study design and subjectsIn this prospective study, CrSO2 was continuously recorded in 21 infants with HIE admitted for TH. Outcome measuresBrain MRI signal abnormalities at 2weeks were scored in individual brain region and classified as none/mild, moderate and severe. 13 infants completed Bayley Scales of Infant Development (BSID) testing at 18–24months. ResultsBetween 24 and 36h of life, there was a significant increase in odds of having moderate-severe brain MRI abnormalities with higher absolute CrSO2 values. Per 10% increase in absolute CrSO2, the odds ratio for moderate-severe brain MRI abnormalities was greatest at 30h (OR 3.78; confidence intervals (CI): 1.23–11.6, p=0.011). CrSO2 increased more rapidly in infants with greater injury seen on MRI (0.20/h for MRI scores 0/1, by 0.48/h for MRI score 2, and by 0.68/h for MRI score 3, p=0.05). At 30h, absolute CrSO2 correlated significantly with abnormal MRI findings in basal ganglia (92% vs. 78%, p=0.001), white matter (88% vs. 76%, p=0.01), posterior limb of internal capsule (92% vs. 78%, p=0.001), and brain stem (94% vs. 80%, p=0.03) but not with cortical injury (86% vs. 80%, p=0.17). Higher CrSO2 beyond 24h correlated with greater odds of worse BSID scores. ConclusionsIncreasing CrSO2 is associated with moderate-severe brain injury as assessed by MRI. Higher absolute CrSO2 values during TH correlates with subcortical injury on MRI and poor neurodevelopmental outcomes in infants with HIE undergoing TH. CrSO2 can inform providers seeking early identification of patients at risk of worse injury who may benefit from further intervention.

The Contribution of Area MT to Visual Motion Perception Depends on Training

Perceptual decisions require the transformation of raw sensory inputs into cortical representations suitable for stimulus discrimination. One of the best-known examples of this transformation involves the middle temporal area (MT) of the primate visual cortex. Area MT provides a robust representation of stimulus motion, and previous work has shown that it contributes causally to performance on motion discrimination tasks. Here we report that the strength of this contribution can be highly plastic: depending on the recent training history, pharmacological inactivation of MT can severely impair motion discrimination, or it can have little detectable influence. Further analysis of neural and behavioral data suggests that training moves the readout of motion information between MT and lower-level cortical areas. These results show that the contribution of individual brain regions to conscious perception can shift flexibly depending on sensory experience.

Diffusion tensor imaging tensor shape analysis for assessment of regional white matter differences.

Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.

Changes in perceptual sensitivity related to spatial cues depends on subcortical activity

Spatial cues allow animals to selectively attend to relevant visual stimuli while ignoring distracters. This process depends on a distributed neuronal network, and an important current challenge is to understand the functional contributions made by individual brain regions within this network and how these contributions interact. Recent findings point to a possible anatomical segregation, with cortical and subcortical brain regions contributing to different functional components of selective attention. Cortical areas, especially visual cortex, may be responsible for implementing changes in perceptual sensitivity by changing the signal-to-noise ratio, whereas other regions, such as the superior colliculus, may be involved in processes that influence selection between competing stimuli without regulating perceptual sensitivity. Such a segregation of function would predict that when activity in the superior colliculus is suppressed by reversible inactivation, animals should still show changes in perceptual sensitivity mediated by the intact cortical circuits. Contrary to this prediction, here we report that inactivation of the primate superior colliculus eliminates the changes in perceptual sensitivity made possible by spatial cues. These findings demonstrate changes in perceptual sensitivity depend not only on neuronal activity in cortex but also require interaction with signals from the superior colliculus.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.

Whole-Brain Neural Dynamics of Probabilistic Reward Prediction.

Predicting future reward is paramount to performing an optimal action. Although a number of brain areas are known to encode such predictions, a detailed account of how the associated representations evolve over time is lacking. Here, we address this question using human magnetoencephalography (MEG) and multivariate analyses of instantaneous activity in reconstructed sources. We overtrained participants on a simple instrumental reward learning task where geometric cues predicted a distribution of possible rewards, from which a sample was revealed 2000 ms later. We show that predicted mean reward (i.e., expected value), and predicted reward variability (i.e., economic risk), are encoded distinctly. Early on, representations of mean reward are seen in parietal and visual areas, and later in frontal regions with orbitofrontal cortex emerging last. Strikingly, an encoding of reward variability emerges simultaneously in parietal/sensory and frontal sources and later than mean reward encoding. An orbitofrontal variability encoding emerged around the same time as that seen for mean reward. Crucially, cross-prediction showed that mean reward and variability representations are distinct and also revealed that instantaneous representations become more stable over time. Across sources, the best fitting metric for variability signals was coefficient of variation (rather than SD or variance), but distinct best metrics were seen for individual brain regions. Our data demonstrate how a dynamic encoding of probabilistic reward prediction unfolds in the brain both in time and space.SIGNIFICANCE STATEMENT Predicting future reward is paramount to optimal behavior. To gain insight into the underlying neural computations, we investigate how reward representations in the brain arise over time. Using magnetoencephalography, we show that a representation of predicted mean reward emerges early in parietal/sensory regions and later in frontal cortex. In contrast, predicted reward variability representations appear in most regions at the same time, and slightly later than for mean reward. For both features, representations dynamically change >1000 ms before stabilizing. The best metric for encoding variability is coefficient of variation, with heterogeneity in this encoding seen between brain areas. The results provide novel insights into the emergence of predictive reward representations.

Stress-Induced Differential Regulation Leads to Decoupling of the Activity between mPFC and Amygdala

Exposure to severe stress leads to the development of psychiatric disorders. Clinical studies have shown that three brain areas involved in learning and memory—the hippocampus, amygdala and medial prefrontal cortex (mPFC)—undergo distinct changes with stress disorders. While the hippocampus and mPFC show impairment in structural and functional changes, the amygdala shows an enhancement. Despite these three brain regions having strong anatomical connections, most of these studies focus on individual brain regions. However, recent studies have shown that these connections between regions have strong functional implications. The connectivity between the mPFC and the amygdala has recently been shown to be crucial for fear expression (Likhtik et al., 2014). The effect of stress on the functional connections between these regions is poorly understood. Therefore, we performed in-vivo local field potential recordings from the mPFC and the amygdala in awake behaving rats during fear expression. We found that stress differentially regulates the activity in the mPFC and the amygdala during fear expression. Consistent with cellular findings, the activity in the amygdala is upregulated by stress during fear expression. However, the activity of the mPFC is unaffected by stress during fear expression. We also found that stress causes a decoupling between the activity in the amygdala and mPFC. Interestingly, an earlier study showed that stress strengthens the coupling between the hippocampus and the amygdala (Ghosh et al., 2013). Therefore, although chronic stress impairs structure and function in both the hippocampus and mPFC, the interactions of these two areas and the amygdala appear to be affected in a contrasting fashion. Functional connectivity gets stronger from amygdala to hippocampus but it gets disrupted between mPFC and amygdala. Future studies need to focus on mechanisms involved in these connectivity changes.

日本語版ToM Localizer for fMRIの開発

Theory-of-mind (ToM) has been extensively studied using neuroimaging, with the goal of finding a neural basis for ToM and its associate emotional and cognitive processes. In neuroimaging, a functional localizer is used when a region of interest needs to be identified in a way that is statistically independent of the main experiment. The original ToM localizer (ToM-L) for functional magnetic resonance imaging (Dodell-Feder et al., 2011) measures brain activity when a set of English sentences and related questions are read and answered by participants. We developed a linguistically localized version of the ToM-L for use with Japanese speakers, and evaluated it by scanning 70 participants. The results showed that this localizer could be used to define individual ToM-related areas, requiring about one-third of the scanning time of the original ToM-L while maintaining its statistical ability to identify individual ToM-related brain regions.

Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks.

In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

A viscoelastic analysis of the P56 mouse brain under large-deformation dynamic indentation.

We present the first mechanical characterization of the viscoelastic response for different regions of mouse brain. Force-relaxation tests are performed under large strain dynamic micro-indentation, and viscoelastic models are used subsequently, providing time-dependent mechanical properties of brain tissue under loading conditions comparable to what is experienced in TBI. The unique mechanical properties of different brain regions are highlighted, with substantial variations in the viscoelastic properties and damping ratio of each region. Cortex and pons were the stiffest regions, while the thalamus and medulla were most compliant. The cerebellum and thalamus had highest damping ratio values and those of the medulla were lowest. The reported material parameters can be implemented into finite element computer models of the mouse to investigate the effects of trauma on individual brain regions.

Proteome rearrangements after auditory learning: high-resolution profiling of synapse-enriched protein fractions from mouse brain.

Learning and memory processes are accompanied by rearrangements of synaptic protein networks. While various studies have demonstrated the regulation of individual synaptic proteins during these processes, much less is known about the complex regulation of synaptic proteomes. Recently, we reported that auditory discrimination learning in mice is associated with a relative down‐regulation of proteins involved in the structural organization of synapses in various brain regions. Aiming at the identification of biological processes and signaling pathways involved in auditory memory formation, here, a label‐free quantification approach was utilized to identify regulated synaptic junctional proteins and phosphoproteins in the auditory cortex, frontal cortex, hippocampus, and striatum of mice 24 h after the learning experiment. Twenty proteins, including postsynaptic scaffolds, actin‐remodeling proteins, and RNA‐binding proteins, were regulated in at least three brain regions pointing to common, cross‐regional mechanisms. Most of the detected synaptic proteome changes were, however, restricted to individual brain regions. For example, several members of the Septin family of cytoskeletal proteins were up‐regulated only in the hippocampus, while Septin‐9 was down‐regulated in the hippocampus, the frontal cortex, and the striatum. Meta analyses utilizing several databases were employed to identify underlying cellular functions and biological pathways. Data are available via ProteomeExchange with identifier PXD003089. How does the protein composition of synapses change in different brain areas upon auditory learning? We unravel discrete proteome changes in mouse auditory cortex, frontal cortex, hippocampus, and striatum functionally implicated in the learning process. We identify not only common but also area‐specific biological pathways and cellular processes modulated 24 h after training, indicating individual contributions of the regions to memory processing.