LIONirs: flexible Matlab toolbox for fNIRS data analysis.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. NEW METHOD: We introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System and available on GitHub. RESULTS: Within the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. COMPARISON WITH EXISTING METHODS: Several good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. CONCLUSIONS: LIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples.

Superparamagnetic iron oxide nanoparticles-based detection of neuronal activity.

Precise detection of brain regions harboring heightened electrical activity plays a central role in the understanding and treatment of diseases such as epilepsy. Superparamagnetic iron oxide nanoparticles (SPIONs) react to magnetic fields by aggregating and represent interesting candidates as new sensors for neuronal magnetic activity. We hypothesized that SPIONs in aqueous solution close to active brain tissue would aggregate proportionally to neuronal activity. We tested this hypothesis using an in vitro model of rat brain slice with different levels of activity. Aggregation was assessed with dynamic light scattering (DLS) and magnetic resonance imaging (MRI). We found that increasing brain slice activity was associated with higher levels of aggregation as measured by DLS and MRI, suggesting that the magnetic fields from neuronal tissue could induce aggregation in nearby SPIONs in solution. MRI signal change induced by SPIONs aggregation could serve as a powerful new tool for detection of brain electrical activity.

Heart Rate Variability in Insulo-Opercular Epilepsy.

BACKGROUND: We aimed to evaluate heart rate variability (HRV) changes in insulo-opercular epilepsy (IOE) and after insulo-opercular surgery. METHODS: We analyzed 5-min resting HRV of IOE patients before and after surgery. Patients' SUDEP-7 risk inventory scores were also calculated. Results were compared with age- and sex-matched patients with temporal lobe epilepsy (TLE) and healthy individuals. RESULTS: There were no differences in HRV measurements between IOE, TLE, and healthy control groups (and within each IOE group and TLE group) in preoperative and postoperative periods. In IOE patients, the SUDEP-7 score was positively correlated with pNN50 (percentage of successive RR intervals that differ by more than 50 ms) (p = 0.008) and RMSSD (root mean square of successive RR interval differences) (p = 0.019). We stratified IOE patients into those whose preoperative RMSSD values were below (Group 1a = 7) versus above (Group 1b = 9) a cut-off threshold of 31 ms (median value of a healthy population from a previous study). In group 1a, all HRV values significantly increased after surgery. In group 1b, time-domain parameters significantly decreased postoperatively. CONCLUSIONS: Our results suggest that in IOE, HRV may be either decreased in parasympathetic tone or increased globally in both sympathetic and parasympathetic tones. We found no evidence that insulo-opercular surgeries lead to major autonomic dysfunction when a good seizure outcome is reached. The increase in parasympathetic tone observed preoperatively may be of clinical concern, as it was positively correlated with the SUDEP-7 score.

Intracranial EEG seizure onset and termination patterns and their association.

OBJECTIVE: The study of seizure onset and termination patterns has the potential to enhance our understanding of the underlying mechanisms of seizure generation and cessation. It is largely unclear whether seizures with distinct onset patterns originate from varying network interactions and terminate through different termination pathways. METHODS: We investigated the morphology and location of 103 intracranial EEG seizure onset and termination patterns from 20 patients with drug-resistant focal epilepsy. We determined if seizure onset patterns were associated with specific termination patterns. Finally, we looked at network interactions prior to the generation of distinct seizure onset patterns by calculating directed functional connectivity matrices. RESULTS: We identified nine seizure onset and six seizure termination patterns. The most common onset pattern was Low-Voltage Fast Activity (36 %), and the most frequent termination pattern was Burst Suppression (44 %). All seizures with fast (>13 Hz) termination patterns had a fast (>13 Hz) onset pattern type. Almost any onset pattern could terminate with the Burst Suppression and rhythmic Spike/PolySpike and Wave (rSW/rPSW) termination patterns. Seizures with a fast activity onset had higher inflow to the seizure onset zone from other regions in the gamma and high gamma frequency ranges prior to their generation compared to seizures with a slow onset. SIGNIFICANCE: Our observations suggest that different mechanisms underlie the generation of different seizure onset patterns although seizure onset patterns can share a common termination pattern. Possible mechanisms underlying these patterns are discussed.

A simulation study investigating potential diffusion-based MRI signatures of microstrokes.

Recent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits. Due to their small size, identifying measurable biomarkers of these microvascular lesions remains a major challenge. This work aims to simulate potential MRI signatures combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). Driving our hypothesis are recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially-oriented, and optical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n = 5) before and after inducing targeted photothrombosis, were analyzed. Computational vascular graphs combined with a 3D Monte-Carlo simulator were used to characterize the magnetic resonance (MR) response, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. We quantified the minimal intravoxel signal loss ratio when applying multiple gradient directions, at varying sequence parameters with and without ASL. With ASL, our results demonstrate a significant difference (p < 0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p < 0.005) using angiograms measured at week 4. Without ASL, no reliable signal change was found. We found that higher ratios, and accordingly improved significance, were achieved at lower magnetic field strengths (e.g., B0 = 3T) and shorter echo time TE (< 16 ms). Our simulations suggest that microstrokes might be characterized through ASL-DWI sequence, providing necessary insights for posterior experimental validations, and ultimately, future translational trials.

Cerebral functional networks during sleep in young and older individuals.

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Feasibility of implantable iron oxide nanoparticles in detecting brain activity-proof of concept in a rat model.

BACKGROUND: Precise detection of zones of increased brain activity is a crucial aspect in the delineation of the cortical region responsible for epilepsy (epileptic focus). When possible, removal of this area can lead to improved control of epilepsy or even its cure. This study explores a new method of detection of electrical brain activity based on the surgical implantation of iron oxide superparamagnetic nanoparticles (SPIONs). By their magnetic nature, SPIONs tend to aggregate in the presence of magnetic fields. This study aims to demonstrate if brain's magnetic fields could change the aggregation status of SPIONs in a rat model. METHODS: Plastic containers (capsules) containing SPIONs in aqueous suspension were implanted over the cortex of either rats rendered epileptic or naive rats (sham). A model of focal epilepsy using cortical penicillin injection was used for the epileptic rats. Capsules not implanted in rats served as control. Using magnetic resonance imaging (MRI), the aggregation status of SPIONs contained in the capsules was assessed via measurement of the T2 relaxivity time of the solutions. RESULTS: Eight Rats were used for the experiments, with 4 rats in each group (epileptic and sham). One Rat in the sham group died immediately after surgery and 3 rats failed to demonstrate the expected behavior after intervention (2 rats in epileptic group with limited observable seizures and 1 rat in the sham group having repeated seizures). T2 of the control capsules were significantly lower than those implanted in rats (146 ms vs 7.6 ms, p < 0.001), suggesting a higher degree of SPIONs aggregation in the implanted capsules. No significant difference in T2 could be demonstrated between epileptic and sham rats. CONCLUSIONS: SPIONs implanted over the cortex of active brain showed an increased aggregation status, confirming their potential as a new marker for brain activity. One of the main advantages of SPIONs is that their aggregation status can be measured at a distance with MRI, taking advantage of its high spatial resolution and imaging capacities. The current model was suboptimal to confirm if epileptic activity can be differentiated from normal brain activity using SPIONs.

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks.

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.

TAO-kinase 3 governs the terminal differentiation of NOTCH2-dependent splenic conventional dendritic cells.

Antigen-presenting conventional dendritic cells (cDCs) are broadly divided into type 1 and type 2 subsets that further adapt their phenotype and function to perform specialized tasks in the immune system. The precise signals controlling tissue-specific adaptation and differentiation of cDCs are currently poorly understood. We found that mice deficient in the Ste20 kinase Thousand and One Kinase 3 (TAOK3) lacked terminally differentiated ESAM+ CD4+ cDC2s in the spleen and failed to prime CD4+ T cells in response to allogeneic red-blood-cell transfusion. These NOTCH2- and ADAM10-dependent cDC2s were absent selectively in the spleen, but not in the intestine of Taok3-/- and CD11c-cre Taok3fl/fl mice. The loss of splenic ESAM+ cDC2s was cell-intrinsic and could be rescued by conditional overexpression of the constitutively active NOTCH intracellular domain in CD11c-expressing cells. Therefore, TAOK3 controls the terminal differentiation of NOTCH2-dependent splenic cDC2s.

TAOK3 is a MAP3K contributing to osteoblast differentiation and skeletal mineralization.

Current anabolic drugs to treat osteoporosis and other disorders of low bone mass all have important limitations in terms of toxicity, contraindications, or poor efficacy in certain contexts. Addressing these limitations will require a better understanding of the molecular pathways, such as the mitogen activated protein kinase (MAPK) pathways, that govern osteoblast differentiation and, thereby, skeletal mineralization. Whereas MAP3Ks functioning in the extracellular signal-regulated kinases (ERK) and p38 pathways have been identified in osteoblasts, MAP3Ks mediating proximal activation of the c-Jun N-terminal kinase (JNK) pathway have yet to be identified. Here, we demonstrate that thousand-and-one kinase 3 (TAOK3, MAP3K18) functions as an upstream activator of the JNK pathway in osteoblasts both in vitro and in vivo. Taok3-deficient osteoblasts displayed defective JNK pathway activation and a marked decrease in osteoblast differentiation markers and defective mineralization, which was also confirmed using TAOK3 deficient osteoblasts derived from human MSCs. Additionally, reduced expression of Taok3 in a murine model resulted in osteopenia that phenocopies aspects of the Jnk1-associated skeletal phenotype such as occipital hypomineralization. Thus, in vitro and in vivo evidence supports TAOK3 as a proximal activator of the JNK pathway in osteoblasts that plays a critical role in skeletal mineralization.

Identification of global and local states during seizures using quantitative functional connectivity and recurrence plot analysis.

INTRODUCTION: As a dynamical system, the brain constantly modulates its state and epileptic seizures have been hypothesized to be low dimensional periodic states of the brain. With this assumption, seizures have previously been investigated to identify patterns of these recurrent states; however, these attempts have generated conflicting results. These discrepant observations led us to reconsider the dynamic of state transitions during seizures. METHODS: Using intracerebral recordings of 17 refractory epilepsy patients assessed prior to surgery, we studied ictal states with several state-of-the-art methods in order to investigate their dynamics. Global states were identified based on distinct functional connectivity measures in the time domain, frequency domain, and phase-space. We further investigated the state transitions in different brain regions locally using a univariate measure based on dynamical system analysis named the Recurrence Plot (RP). RESULTS: For the ictal period, we detected lower global state transition rates compared to pre- and post-ictal periods (p < 0.05 for seizure-free (SF) and p > 0.05 for non-seizure-free (NSF) groups post-surgery); however, the structure of RPs pointed towards higher state transition rates in some regions like the seizure-onset-zone (p < 0.001 for SF and p > 0.05 for NSF group). Moreover, a direct comparison of state transition dynamics between SF and NSF patients revealed different patterns for local state transitions between SF and NSF patients (p < 0.05 for seizure-onset-zone while p > 0.05 for other regions) and no significant difference in global state transition rates (p > 0.05). CONCLUSION: Our findings pointed to distinct dynamics for state transitions at different spatial scales. While the pattern of global state transitions led to the conclusion that the brain changes state less frequently during ictal activity, locally, it experienced a higher rate of state transition. Furthermore, our results for different patterns of state transitions in the seizure-onset-zone between SF and NSF patients could have a practical application in predicting surgical outcome.

Large-Scale Desynchronization During Interictal Epileptic Discharges Recorded With Intracranial EEG.

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.

There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; however, recent publications have suggested that functional Near-Infrared Spectroscopy (fNIRS), a relatively new technique, could be used to predict seizures. The objectives of this research are to show that the application of fNIRS to epileptic seizure detection yields results that are superior to those based on EEG and to demonstrate that the application of deep learning to this problem is suitable given the nature of fNIRS recordings. A Convolutional Neural Network (CNN) is applied to the prediction of epileptic seizures from fNIRS signals, an optical modality for recording brain waves. The implementation of the proposed method is presented in this work. Application of CNN to fNIRS recordings showed an accuracy ranging between 96.9% and 100%, sensitivity between 95.24% and 100%, specificity between 98.57% and 100%, a positive predictive value between 98.52% and 100%, and a negative predictive value between 95.39% and 100%. The most important aspect of this research is the combination of fNIRS signals with the particular CNN algorithm. The fNIRS modality has not been used in epileptic seizure prediction. A CNN is suitable for this application because fNIRS recordings are high dimensional data and they can be modeled as three-dimensional tensors for classification.

Assessing therapeutic response non-invasively in a neonatal rat model of acute inflammatory white matter injury using high-field MRI.

Perinatal infection and inflammatory episodes in preterm infants are associated with diffuse white matter injury (WMI) and adverse neurological outcomes. Inflammation-induced WMI was previously shown to be linked with later hippocampal atrophy as well as learning and memory impairments in preterm infants. Early evaluation of injury load and therapeutic response with non-invasive tools such as multimodal magnetic resonance imaging (MRI) would greatly improve the search of new therapeutic approaches in preterm infants. Our aim was to evaluate the potential of multimodal MRI to detect the response of interleukin-1 receptor antagonist (IL-1Ra) treatment, known for its neuroprotective properties, during the acute phase of injury on a model of neonatal WMI. Rat pups at postnatal day 3 (P3) received intracerebral injection of lipopolysaccharide with systemic IL-1Ra therapy. 24 h later (P4), rats were imaged with multimodal MRI to assess microstructure by diffusion tensor imaging (DTI) and neurochemical profile of the hippocampus with 1H-magnetic resonance spectroscopy. Astrocyte and microglial activation, apoptosis and the mRNA expression of pro-inflammatory and necroptotic markers were assessed. During the acute phase of injury, neonatal LPS exposure altered the concentration of hippocampus metabolites related to neuronal integrity, neurotransmission and membrane integrity and induced diffusivity restriction. Just 24 h after initiation of therapy, early indication of IL-1Ra neuroprotective effect could be detected in vivo by non-invasive spectroscopy and DTI, and confirmed with immunohistochemical evaluation and mRNA expression of inflammatory markers and cell death. In conclusion, multimodal MRI, particularly DTI, can detect not only injury but also the acute therapeutic effect of IL-1Ra suggesting that MRI could be a useful non-invasive tool to follow, at early time points, the therapeutic response in preterm infants.

Systolic hypertension-induced neurovascular unit disruption magnifies vascular cognitive impairment in middle-age atherosclerotic LDLr-/-:hApoB+/+ mice.

Cognitive functions are dependent upon intercommunications between the cellular components of the neurovascular unit (NVU). Vascular risk factors are associated with a more rapid rate of cognitive decline with aging and cerebrovascular diseases magnify both the incidence and the rate of cognitive decline. The causal relationship between vascular risk factors and injury to the NVU is, however, lacking. We hypothesized that vascular risk factors, such as hypertension and dyslipidemia, promote disruption of the NVU leading to early cognitive impairment. We compared brain structure and cerebrovascular functions of 1-year old (middle-aged) male wild-type (WT) and atherosclerotic hypertensive (LDLr-/-:hApoB+/+, ATX) mice. In addition, mice were subjected, or not, to a transverse aortic constriction (TAC) for 6 weeks to assess the acute impact of an increase in systolic blood pressure on the NVU and cognitive functions. Compared with WT mice, ATX mice prematurely developed cognitive decline associated with cerebral micro-hemorrhages, loss of microvessel density and brain atrophy, cerebral endothelial cell senescence and dysfunction, brain inflammation, and oxidative stress associated with blood-brain barrier leakage and brain hypoperfusion. These data suggest functional disturbances in both vascular and parenchymal components of the NVU. Exposure to TAC-induced systolic hypertension promoted cerebrovascular damage and cognitive decline in WT mice, similar to those observed in sham-operated ATX mice; TAC exacerbated the existing cerebrovascular dysfunctions and cognitive failure in ATX mice. Thus, a hemodynamic stress such as systolic hypertension could initiate the cascade involving cerebrovascular injury and NVU deregulation and lead to cognitive decline, a process accelerated in atherosclerotic mice.

Non-Alcoholic Fatty Liver Disease, and the Underlying Altered Fatty Acid Metabolism, Reveals Brain Hypoperfusion and Contributes to the Cognitive Decline in APP/PS1 Mice.

Non-alcoholic fatty liver disease (NAFLD), the leading cause of chronic liver disease, is associated with cognitive decline in middle-aged adults, but the mechanisms underlying this association are not clear. We hypothesized that NAFLD would unveil the appearance of brain hypoperfusion in association with altered plasma and brain lipid metabolism. To test our hypothesis, amyloid precursor protein/presenilin-1 (APP/PS1) transgenic mice were fed a standard diet or a high-fat, cholesterol and cholate diet, inducing NAFLD without obesity and hyperglycemia. The diet-induced NAFLD disturbed monounsaturated and polyunsaturated fatty acid (MUFAs, PUFAs) metabolism in the plasma, liver, and brain, and particularly reduced n-3 PUFAs levels. These alterations in lipid homeostasis were associated in the brain with an increased expression of Tnfα, Cox2, p21, and Nox2, reminiscent of brain inflammation, senescence, and oxidative stress. In addition, compared to wild-type (WT) mice, while brain perfusion was similar in APP/PS1 mice fed with a chow diet, NAFLD in APP/PS1 mice reveals cerebral hypoperfusion and furthered cognitive decline. NAFLD reduced plasma ß40- and ß42-amyloid levels and altered hepatic but not brain expression of genes involved in ß-amyloid peptide production and clearance. Altogether, our results suggest that in a mouse model of Alzheimer disease (AD) diet-induced NAFLD contributes to the development and progression of brain abnormalities through unbalanced brain MUFAs and PUFAs metabolism and cerebral hypoperfusion, irrespective of brain amyloid pathology that may ultimately contribute to the pathogenesis of AD.

MK5 haplodeficiency decreases collagen deposition and scar size during post-myocardial infarction wound repair.

MK5 is a protein serine/threonine kinase activated by p38, ERK3, and ERK4 MAPKs. MK5 mRNA and immunoreactivity are detected in mouse cardiac fibroblasts, and MK5 haplodeficiency attenuates the increase in collagen 1-α1 mRNA evoked by pressure overload. The present study examined the effect of MK5 haplodeficiency on reparative fibrosis following myocardial infarction (MI). Twelve-week-old MK5+/- and wild-type littermate (MK5+/+) mice underwent ligation of the left anterior descending coronary artery (LADL). Surviving mice were euthanized 8 or 21 days post-MI. Survival rates did not differ significantly between MK5+/+ and MK5+/- mice, with rupture of the LV wall being the primary cause of death. Echocardiographic imaging revealed similar increases in LV end-diastolic diameter, myocardial performance index, and wall motion score index in LADL-MK5+/+ and LADL-MK5+/- mice. Area at risk did not differ between LADL-MK5+/+ and LADL-MK5+/- hearts. In contrast, infarct size, scar area, and scar collagen content were reduced in LADL-MK5+/- hearts. Immunohistochemical analysis of mice experiencing heart rupture revealed increased MMP-9 immunoreactivity in the infarct border zone of LADL-MK5+/- hearts compared with LADL-MK5+/+. Although inflammatory cell infiltration was similar in LADL-MK5+/+ and LADL-MK5+/- hearts, angiogenesis was more pronounced in the infarct border zone of LADL-MK5+/- mice. Characterization of ventricular fibroblasts revealed reduced motility and proliferation in fibroblasts isolated from MK5-/- mice compared with those from both wild-type and haplodeficient mice. siRNA-mediated knockdown of MK5 in fibroblasts from wild-type mice also impaired motility. Hence, reduced MK5 expression alters fibroblast function and scar morphology but not mortality post-MI. NEW & NOTEWORTHY MK5/PRAK is a protein serine/threonine kinase activated by p38 MAPK and/or atypical MAPKs ERK3/4. MK5 haplodeficiency reduced infarct size, scar area, and scar collagen content post-myocardial infarction. Motility and proliferation were reduced in cultured MK5-null cardiac myofibroblasts.

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings.

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

A Pilot Study Investigating Changes in Capillary Hemodynamics and Its Modulation by Exercise in the APP-PS1 Alzheimer Mouse Model.

Dysfunction in neurovascular coupling that results in a mismatch between cerebral blood flow and neuronal activity has been suggested to play a key role in the pathogenesis of Alzheimer's disease (AD). Meanwhile, physical exercise is a powerful approach for maintaining cognitive health and could play a preventive role against the progression of AD. Given the fundamental role of capillaries in oxygen transport to tissue, our pilot study aimed to characterize changes in capillary hemodynamics with AD and AD supplemented by exercise. Exploiting two-photon microscopy, intrinsic signal optical imaging, and magnetic resonance imaging, we found hemodynamic alterations and lower vascular density with AD that were reversed by exercise. We further observed that capillary properties were branch order-dependent and that stimulation-evoked changes were attenuated with AD but increased by exercise. Our study provides novel indications into cerebral microcirculatory disturbances with AD and the modulating role of voluntary exercise on these alterations.

Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy.

Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 µm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.