Search

Functional magnetic resonance imaging (fMRI) is widely used by researchers to noninvasively monitor brain-wide activity. The traditional assumption of a uniform relationship between neuronal and hemodynamic activity throughout the brain has been increasingly challenged. This relationship is now believed to be impacted by heterogeneously distributed cell types and neurochemical signaling. To date, most cell-type- and neurotransmitter-specific influences on hemodynamics have been examined within the cortex and hippocampus of rodent models, where glutamatergic signaling is prominent. However, neurochemical influences on hemodynamics are relatively unknown in largely GABAergic brain regions such as the rodent caudate putamen (CPu). Given the extensive contribution of CPu function and dysfunction to behavior, and the increasing focus on this region in fMRI studies, improved understanding of CPu hemodynamics could have broad impacts. Here we discuss existing findings on neurochemical contributions to hemodynamics as they may relate to the CPu with special consideration for how these contributions could originate from various cell types and circuits. We hope this review can help inform the direction of future studies as well as interpretation of fMRI findings in the CPu.

Alzheimer’s disease (AD) is a serious neurodegenerative disorder without a clear understanding of the etiology and pathophysiology. Recent experimental data has suggested excitation-inhibition (E-I) imbalance as an essential element and critical regulator of AD pathology, but E-I imbalance has not been systematically mapped out in both local and large-scale neuronal circuits in AD. Using a multiscale neural model inversion framework, we identified disrupted E-I balance as well as impaired excitatory and inhibitory connections in a large network during AD progression based on resting-state functional MRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We observed that E-I balance is progressively disrupted from mild cognitive impairment (MCI) to AD and alteration of E-I balance is bidirectional varying from region to region. Also, we found that inhibitory connections are more significantly impaired than excitatory connections and the strength of the majority of excitatory and inhibitory connections reduces in MCI and AD, leading to gradual decoupling of neural populations. Moreover, we revealed a core AD network comprising mainly of limbic and cingulate regions including the hippocampus, pallidum, putamen, nucleus accumbens, inferior temporal cortex and caudal anterior cingulate cortex. These brain regions exhibit consistent and stable E-I alteration across MCI and AD, which may represent a stable AD biomarker and an important therapeutic target. Overall, our study constitutes the first attempt to delineate E-I imbalance in large-scale neuronal circuits during AD progression, which facilitates the development of new treatment paradigms to restore pathological E-I balance in AD.

SummaryThe default mode network (DMN) is closely associated with self-referential mental functions and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber-photometry with fMRI and computational modeling to characterize dynamics of putative rodent DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. We reveal neuronal activity changes in AI and DMN nodes prior to fMRI-derived DMN activations and uncover cyclical transition patterns between spatiotemporal neuronal activity states. Finally, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity, and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate previously unknown properties regarding the neurobiological foundations of the rodent DMN and its modulation by salient stimuli, paving the way for future translational studies.HighlightsConcurrent measurement of neuronal (GCaMP) and fMRI signals in retrosplenial, cingulate, prelimbic, and anterior insula corticesGCaMP signals reveal neuronal antagonism between AI and fMRI-derived DMN activation and deactivationGCaMP signals reveal salient oddball stimuli-induced suppression of prelimbic, cingulate and retrosplenial cortices, and activation of anterior insular cortexAnterior insular cortex causally inhibits retrosplenial cortex during processing of salient oddball stimuliFindings delineate neurofunctional organization of the rodent DMN and provide a more informed model for translational studies

This paper presents a new design and fabrication method of flexible neural microprobes for deep brain stimulations (DBS) and neurological treatment applications. The developed new flexible microprobes are compatible with functional magnetic resonance imaging (fMRI) and can be used for neurological studies of brain functions under functional imaging such as fMRI for a long period of time. In this paper, the materials of the flexible neural microprobes are comprehensively selected to minimize the magnetic resonance imaging (MRI) artifacts, which limits many conventional feasible manufacturing processes to be used. Polyimide was adopted for the substrate of the neural microprobe, which has good biocompatibility and a relatively lower Young’s modulus. A 200 nm chromium reinforcement layer was embedded in the microprobe to attenuate its implantation stiffness while remaining flexible. Gold electroplating was employed to modify the electrode sites to improve the signal quality and sensitivity. The designed new neural microprobes were successfully fabricated at our NCSU Nanofabrication Facility (NNF) and bonded to a customized PCB. After the fabrication, the developed neural microprobes were characterized to validate their effectiveness. In vivo simultaneous DBS-fMRI experiments were conducted in surgery rooms on a rat’s deep brain targets, demonstrating the potential applications of our neural microprobe for neuroscience studies, medical diagnosis, and treatment applications. In vivo electrophysiology results show the effectiveness of our fabricated neural microprobes.

The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the rat brain. Optogenetic stimulation of Chronos-expressing AI neurons in rats suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.TeaserInsular cortex stimulation reveals causal mechanisms underlying default mode network suppression by the salience network

The default mode network (DMN) of the brain is involved in cognition, emotion regulation, impulsivity, and balancing between internally and externally focused states. DMN dysregulation has been implicated in several neurological and neuropsychiatric disorders. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber-photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the DMN in mice. Chemogenetic-induced tonic LC-NE activity decreased cerebral blood volume (CBV) and glucose uptake, and increased synchronous low frequency fMRI activity within the frontal cortices of the DMN. Fiber-photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional stimulus-evoked coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows comparison with invasive or terminal procedures. To date, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. We introduce StandardRat, a consensus rat functional MRI acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired in rats from 46 centers. We developed a reproducible pipeline for the analysis of rat data acquired with diverse protocols and determined experimental and processing parameters associated with a more robust functional connectivity detection. We show that the standardized protocol enhances biologically plausible functional connectivity patterns, relative to pre-existing acquisitions. The protocol and processing pipeline described here are openly shared with the neuroimaging community to promote interoperability and cooperation towards tackling the most important challenges in neuroscience.

The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high temporal resolution and spatial specificity inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.

Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community.Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

Fiber photometry is an emerging technique for recording fluorescent sensor activity in the brain. However, significant hemoglobin absorption artifacts in fiber photometry data may be misinterpreted as sensor activity changes. Because hemoglobin exists widely in the brain, and its concentration varies temporally, such artifacts could impede the accuracy of photometry recordings. Here we present use of spectral photometry and computational methods to quantify photon absorption effects by using activity-independent fluorescence signals, which can be used to derive oxy- and deoxy-hemoglobin concentration changes. Although these changes are often temporally delayed compared with the fast-responding fluorescence spikes, we found that erroneous interpretation may occur when examining pharmacology-induced sustained changes and that sometimes hemoglobin absorption could flip the GCaMP signal polarity. We provide hemoglobin-based correction methods to restore fluorescence signals and compare our results with other commonly used approaches. We also demonstrated the utility of spectral fiber photometry for delineating regional differences in hemodynamic response functions.

Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation (RATS), Pulse-Coupled Neural Network (PCNN), SHape descriptor selected External Regions after Morphologically filtering (SHERM), and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community.Significant methodological contributionWe proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

The loss of central norepinephrine (NE) released by neurons of the locus coeruleus (LC) occurs with aging, and is thought to be an important factor in producing the many of the nonmotor symptoms and exacerbating the degenerative process in animal models of Parkinson’s disease (PD). We hypothesize that selectively depleting noradrenergic LC neurons prior to the induction of chronic neuroinflammation may not only accelerate the rate of progressive neurodegeneration throughout the brain, but may exacerbate nonmotor and motor behavioral phenotypes that recapitulate symptoms of PD. For this reason, we used a “two-hit” mouse model whereby brain NE were initially depleted by DSP-4 one week prior to exposing mice to LPS. We found that pretreatment with DSP-4 potentiated LPS-induced sequential neurodegeneration in SNpc, hippocampus, and motor cortex, but not in VTA and caudate/putamen. Mechanistic study revealed that DSP-4 enhanced LPS-induced microglial activation and subsequently elevated neuronal oxidative stress in affected brain regions in a time-dependent pattern. To further characterize the effects of DSP-4 on non-motor and motor symptoms in the LPS model, physiological and behavioral tests were performed at different time points following injection. Consistent with the enhanced neurodegeneration, DSP-4 accelerated the progressive deficits of non-motor symptoms including hyposmia, constipation, anxiety, sociability, exaggerated startle response and impaired learning. Furthermore, notable decreases of motor functions, including decreased rotarod activity, grip strength, and gait disturbance, were observed in treated mice. In summary, our studies provided not only an accelerated “two-hit” PD model that recapitulates the features of sequential neuron loss and the progression of motor/non-motor symptoms of PD, but also revealed the critical role of early LC noradrenergic neuron damage in the pathogenesis of PD-like symptoms.

SummaryFiber-photometry is an emerging technique for recording fluorescent sensor activity in the brain. However, significant hemoglobin-absorption artifacts in fiber-photometry data may be misinterpreted as sensor activity changes. Because hemoglobin exists in nearly every location in the brain and its concentration varies over time, such artifacts could impede the accuracy of many photometry recording results. Here we present a novel use of spectral photometry technique and propose computational methods to quantify photon absorption effects by using activity-independent fluorescence signals, which can be used to derive oxy- and deoxy-hemoglobin concentration changes. Following time-locked neuronal activationin vivo, we observed that a 20% increase in CBV contributes to about a 4% decrease in green fluorescence signal and a 2% decrease in red fluorescence signal. While these hemoglobin concentration changes are often temporally delayed than the fast-responding fluorescence spikes, we found that erroneous interpretation may occur when examining pharmacology-induced sustained activity changes, and in some cases, hemoglobin-absorption could flip the GCaMP signal polarity. We provided hemoglobin-based correction methods to restore fluorescence signals across spectra and compare our results against the commonly used regression approach. We also demonstrated the utility of spectral fiber-photometry for delineating brain regional differences in hemodynamic response functions.HighlightsHemoglobin-absorption compromises fiber-photometry recordingin vivoSpectral photometry allows quantification of hemoglobin concentration changes for correctionThe proposed platform allows measuring regional differences in neurovascular transfer function

This paper presents vibration-assisted insertion of flexible neural electrodes with bio-dissolvable guides to deliver accurate microprobe insertion with minimized tissue damage. Invasive flexible neural microprobe is an important new tool for neuromodulation and recording research for medical neurology treatment applications. Flexible neural electrode probes are susceptible to bending and buckling during surgical implantation due to the thin and flexible soft substrates. Inspired by insects in nature, a vibration-assisted insertion technique is developed for flexible neural electrode insertion to deliver accurate microprobe insertion with minimized tissue damage. A three-dimensional combined longitudinal-twisting (L&T) vibration is used to reduce the insertion friction force, and thus reducing soft tissue damage. To reduce the flexible microelectrode buckling during surgical insertion, a bio-dissolvable Polyethylene glycol (PEG) guide is developed for the enhancement of flexible neural probe stiffness. Combining these two methods, the insertion performance of the flexible neural probe is significantly improved. Both the in vitro and the in vivo experiments were conducted to validate the proposed techniques.

The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high spatiotemporal resolution inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.

PURPOSE: QuEnch-assiSTed (QUEST) MR provides a unique biomarker of excessive production of paramagnetic free radicals (oxidative stress) in vivo. The contribution from superoxide, a common upstream species found in oxidative stress-based disease, to the QUEST metric is unclear. Here, we begin to address this question by measuring superoxide spin-lattice relaxivity (r1) in phantoms. METHODS: Stable superoxide free radicals were generated in water phantoms of potassium superoxide ([Formula: see text]. To measure r1, 1/T1 of different concentration solutions of [Formula: see text] in the presence and absence of the antioxidant superoxide dismutase were measured. 1/T1 confounding factors including acquisition sequence, pH, and water source were also evaluated. RESULTS: T1-weighted signal intensity increased with [Formula: see text] concentration. No contribution from pH, or reaction products other than superoxide, were noted on 1/T1. Superoxide r1 was measured to be 0.29 mM(−1)s(−1), in agreement with that reported for paramagnetic molecular oxygen and nitroxide free radicals. CONCLUSION: Our first-in-kind measurement of superoxide free radical r1 suggests a detection sensitivity of QUEST MR on the order of tens of [Formula: see text] , within reported level of free radical production during oxidative stress in-vivo. Similar studies for other common free radicals are needed.

Fiber-photometry is an emerging technique for recording fluorescent sensor activity in the brain. Here we demonstrate significant hemoglobin-absorption artifacts that may be misinterpreted as sensor activity changes. Because hemoglobin exists in nearly every location in the brain and its concentration varies over time, such artifacts could significantly impede the accuracy of photometry recording results. We present a novel use of spectral photometry technique and propose a computational method to quantify photon absorption effect from an activity-independent fluorescent protein, which can be used to derive oxy- and deoxy-hemoglobin concentration changes and correct target sensor activities across spectra. We further demonstrate the utility of this method for delineating brain regional differences in neurovascular transfer functions and shed light on the interpretation of hemodynamic-based neuroimaging data.

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.