Evolution of aberrant brain-wide spatiotemporal dynamics of resting-state networks in a Huntington's disease mouse model.

BACKGROUND: Huntington's disease (HD) is marked by irreversible loss of neuronal function for which currently no availability for disease-modifying treatment exists. Advances in the understanding of disease progression can aid biomarker development, which in turn can accelerate therapeutic discovery. METHODS: We characterised the progression of altered dynamics of whole-brain network states in the zQ175DN mouse model of HD using a dynamic functional connectivity (FC) approach to resting-state fMRI and identified quasi-periodic patterns (QPPs) of brain activity constituting the most prominent resting-state networks. RESULTS: The occurrence of the normative QPPs, as observed in healthy controls, was reduced in the HD model as the phenotype progressed. This uncovered progressive cessation of synchronous brain activity with phenotypic progression, which is not observed with the conventional static FC approaches. To better understand the potential underlying cause of the observed changes in these brain states, we further assessed how mutant huntingtin (mHTT) protein deposition affects astrocytes and pericytes - one of the most important effectors of neurovascular coupling, along phenotypic progression. Increased cell-type dependent mHTT deposition was observed at the age of onset of motor anomalies, in the caudate putamen, somatosensory and motor cortex, regions that are prominently involved in HD pathology as seen in humans. CONCLUSION: Our findings provide meaningful insights into the development and progression of altered functional brain dynamics in this HD model and open new avenues in assessing the dynamics of whole brain states, through QPPs, in clinical HD research. HIGHLIGHTS: Hyperactivity in the LCN-linked regions within short QPPs observed before motor impairment onset. DMLN QPP presents a progressive decrease in DMLN activity and occurrence along HD-like phenotype development. Breakdown of the LCN DMLN state flux at motor onset leads to a subsequent absence of the LCN DMLN QPP at an advanced HD-like stage.

Recurrence quantification analysis of rs-fMRI data: A method to detect subtle changes in the TgF344-AD rat model.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is one of the leading causes of dementia, affecting the world's population at a growing rate. The preclinical stage of AD lasts over a decade, hence understanding AD-related early neuropathological effects on brain function at this stage facilitates early detection of the disease. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) has been a powerful tool for understanding brain function, and it has been widely used in AD research. In this study, we apply Recurrence Quantification Analysis (RQA) on rs-fMRI images of 4-months (4 m) and 6-months-old (6 m) TgF344-AD rats and WT littermates to identify changes related to the AD phenotype and aging. RQA has been focused on areas of the default mode-like network (DMLN) and was performed based on Recurrence Plots (RP). RP is a mathematical representation of any dynamical system that evolves over time as a set of its state recurrences. In this paper, RPs were extracted in order to identify the affected regions of the DMLN at very early stages of AD. RESULTS: Using the RQA approach, we identified significant changes related to the AD phenotype at 4 m and/or 6 m in several areas of the rat DMLN including the BFB, Hippocampal fields CA1 and CA3, CG1, CG2, PrL, PtA, RSC, TeA, V1, V2. In addition, with age, brain activity of WT rats showed less predictability, while the AD rats presented reduced decline of predictability. CONCLUSIONS: The results of this study demonstrate that RQA of rs-fMRI data is a potent approach that can detect subtle changes which might be missed by other methodologies due to the brain's non-linear dynamics. Moreover, this study provides helpful information about specific areas involved in AD pathology at very early stages of the disease in a very promising rat model of AD. Our results provide valuable information for the development of early detection methods and novel diagnosis tools for AD.

Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations.

This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer's disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer's disease.

Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression.

Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.

Auditory cortical regions show resting-state functional connectivity with the default mode-like network in echolocating bats.

Echolocating bats are among the most social and vocal of all mammals. These animals are ideal subjects for functional MRI (fMRI) studies of auditory social communication given their relatively hypertrophic limbic and auditory neural structures and their reduced ability to hear MRI gradient noise. Yet, no resting-state networks relevant to social cognition (e.g., default mode-like networks or DMLNs) have been identified in bats since there are few, if any, fMRI studies in the chiropteran order. Here, we acquired fMRI data at 7 Tesla from nine lightly anesthetized pale spear-nosed bats (Phyllostomus discolor). We applied independent components analysis (ICA) to reveal resting-state networks and measured neural activity elicited by noise ripples (on: 10 ms; off: 10 ms) that span this species' ultrasonic hearing range (20 to 130 kHz). Resting-state networks pervaded auditory, parietal, and occipital cortices, along with the hippocampus, cerebellum, basal ganglia, and auditory brainstem. Two midline networks formed an apparent DMLN. Additionally, we found four predominantly auditory/parietal cortical networks, of which two were left-lateralized and two right-lateralized. Regions within four auditory/parietal cortical networks are known to respond to social calls. Along with the auditory brainstem, regions within these four cortical networks responded to ultrasonic noise ripples. Iterative analyses revealed consistent, significant functional connectivity between the left, but not right, auditory/parietal cortical networks and DMLN nodes, especially the anterior-most cingulate cortex. Thus, a resting-state network implicated in social cognition displays more distributed functional connectivity across left, relative to right, hemispheric cortical substrates of audition and communication in this highly social and vocal species.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology. Methods: Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioral hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. Results: We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterized by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Discussion: Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.

Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model.

The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6 months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.

Alterations in theta-gamma coupling and sharp wave-ripple, signs of prodromal hippocampal network impairment in the TgF344-AD rat model.

Alzheimer's disease (AD) is a severe neurodegenerative disorder caused by the accumulation of toxic proteins, amyloid-beta (Aß) and tau, which eventually leads to dementia. Disease-modifying therapies are still lacking, due to incomplete insights into the neuropathological mechanisms of AD. Synaptic dysfunction is known to occur before cognitive symptoms become apparent and recent studies have demonstrated that imbalanced synaptic signaling drives the progression of AD, suggesting that early synaptic dysfunction could be an interesting therapeutic target. Synaptic dysfunction results in altered oscillatory activity, which can be detected with electroencephalography and electrophysiological recordings. However, the majority of these studies have been performed at advanced stages of AD, when extensive damage and cognitive symptoms are already present. The current study aimed to investigate if the hippocampal oscillatory activity is altered at pre-plaque stages of AD. The rats received stereotactic surgery to implant a laminar electrode in the CA1 layer of the right hippocampus. Electrophysiological recordings during two consecutive days in an open field were performed in 4-5-month-old TgF344-AD rats when increased concentrations of soluble Aß species were observed in the brain, in the absence of Aß-plaques. We observed a decreased power of high theta oscillations in TgF344-AD rats compared to wild-type littermates. Sharp wave-ripple (SWR) analysis revealed an increased SWR power and a decreased duration of SWR during quiet wake in TgF344-AD rats. The alterations in properties of SWR and the increased power of fast oscillations are suggestive of neuronal hyperexcitability, as has been demonstrated to occur during presymptomatic stages of AD. In addition, decreased strength of theta-gamma coupling, an important neuronal correlate of memory encoding, was observed in the TgF344-AD rats. Theta-gamma phase amplitude coupling has been associated with memory encoding and the execution of cognitive functions. Studies have demonstrated that mild cognitive impairment patients display decreased coupling strength, similar to what is described here. The current study demonstrates altered hippocampal network activity occurring at pre-plaque stages of AD and provides insights into prodromal network dysfunction in AD. The alterations observed could aid in the detection of AD during presymptomatic stages.

CHalf: Folding Stability Made Simple.

The structure of a protein defines its function and integrity and correlates with the protein folding stability (PFS). Quantifying PFS allows researchers to assess differential stability of proteins in different disease or ligand binding states, providing insight into protein efficacy and potentially serving as a metric of protein quality. There are a number of mass spectrometry (MS)-based methods to assess PFS, such as Thermal Protein Profiling (TPP), Stability of Proteins from Rates of Oxidation (SPROX), and Iodination Protein Stability Assay (IPSA). Despite the critical value that PFS studies add to the understanding of mechanisms of disease and treatment development, proteomics research is still primarily dominated by concentration-based studies. We found that a major reason for the lack of PFS studies is the lack of a user-friendly data processing tool. Here we present the first user-friendly software, CHalf, with a graphical user interface for calculating PFS. Besides calculating site-specific PFS of a given protein from chemical denature folding stability assays, CHalf is also compatible with thermal denature folding stability assays. CHalf also includes a set of data visualization tools to help identify changes in PFS across protein sequences and in between different treatment conditions. We expect the introduction of CHalf to lower the barrier of entry for researchers to investigate PFS, promoting the usage of PFS in studies. In the long run, we expect this increase in PFS research to accelerate our understanding of the pathogenesis and pathophysiology of disease.

Quantifying In Situ Structural Stabilities of Human Blood Plasma Proteins Using a Novel Iodination Protein Stability Assay.

Many of the diseases that plague society today are driven by a loss of protein quality. One method to quantify protein quality is to measure the protein folding stability (PFS). Here, we present a novel mass spectrometry (MS)-based approach for PFS measurement, iodination protein stability assay (IPSA). IPSA quantifies the PFS by tracking the surface-accessibility differences of tyrosine, histidine, methionine, and cysteine under denaturing conditions. Relative to current methods, IPSA increases protein coverage and granularity to track the PFS changes of a protein along its sequence. To our knowledge, this study is the first time the PFS of human serum proteins has been measured in the context of the blood serum (in situ). We show that IPSA can quantify the PFS differences between different transferrin iron-binding states in near in vivo conditions. We also show that the direction of the denaturation curve reflects the in vivo surface accessibility of the amino acid residue and reproducibly reports a residue-specific PFS. Along with IPSA, we introduce an analysis tool Chalf that provides a simple workflow to calculate the residue-specific PFS. The introduction of IPSA increases the potential to use protein structural stability as a structural quality metric in understanding the etiology and progression of human disease. Data is openly available at Chorusproject.org (project ID 1771).

Altered basal forebrain function during whole-brain network activity at pre- and early-plaque stages of Alzheimer's disease in TgF344-AD rats.

BACKGROUND: Imbalanced synaptic transmission appears to be an early driver in Alzheimer's disease (AD) leading to brain network alterations. Early detection of altered synaptic transmission and insight into mechanisms causing early synaptic alterations would be valuable treatment strategies. This study aimed to investigate how whole-brain networks are influenced at pre- and early-plague stages of AD and if these manifestations are associated with concomitant cellular and synaptic deficits.  METHODS: To this end, we used an established AD rat model (TgF344-AD) and employed resting state functional MRI and quasi-periodic pattern (QPP) analysis, a method to detect recurrent spatiotemporal motifs of brain activity, in parallel with state-of-the-art immunohistochemistry in selected brain regions. RESULTS: At the pre-plaque stage, QPPs in TgF344-AD rats showed decreased activity of the basal forebrain (BFB) and the default mode-like network. Histological analyses revealed increased astrocyte abundance restricted to the BFB, in the absence of amyloid plaques, tauopathy, and alterations in a number of cholinergic, gaba-ergic, and glutamatergic synapses. During the early-plaque stage, when mild amyloid-beta (Aß) accumulation was observed in the cortex and hippocampus, QPPs in the TgF344-AD rats normalized suggesting the activation of compensatory mechanisms during this early disease progression period. Interestingly, astrogliosis observed in the BFB at the pre-plaque stage was absent at the early-plaque stage. Moreover, altered excitatory/inhibitory balance was observed in cortical regions belonging to the default mode-like network. In wild-type rats, at both time points, peak activity in the BFB preceded peak activity in other brain regions-indicating its modulatory role during QPPs. However, this pattern was eliminated in TgF344-AD suggesting that alterations in BFB-directed neuromodulation have a pronounced impact in network function in AD. CONCLUSIONS: This study demonstrates the value of rsfMRI and advanced network analysis methods to detect early alterations in BFB function in AD, which could aid early diagnosis and intervention in AD. Restoring the global synaptic transmission, possibly by modulating astrogliosis in the BFB, might be a promising therapeutic strategy to restore brain network function and delay the onset of symptoms in AD.

Preventive care: vaccines matter.

A database of information about more than 30,000 patients verified improved morbidity and mortality due to vaccines and preventive health care in prospective trials.

Cholinergic Modulation of the Default Mode Like Network in Rats.

The discovery of the default mode network (DMN), a large-scale brain network that is suppressed during attention-demanding tasks, had major impact in neuroscience. This network exhibits an antagonistic relationship with attention-related networks. A better understanding of the processes underlying modulation of DMN is imperative, as this network is compromised in several neurological diseases. Cholinergic neuromodulation is one of the major regulatory networks for attention, and studies suggest a role in regulation of the DMN. In this study, we unilaterally activated the right basal forebrain cholinergic neurons and observed decreased right intra-hemispheric and interhemispheric FC in the default mode like network (DMLN). Our findings provide critical insights into the interplay between cholinergic neuromodulation and DMLN, demonstrate that differential effects can be exerted between the two hemispheres by unilateral stimulation, and open windows for further studies involving directed modulations of DMN in treatments for diseases demonstrating compromised DMN activity.

Biomimetic scaffolds with three-dimensional undulated microtopographies.

Many human and animal tissues naturally possess three-dimensional (3D) micro-scale geometries enabling certain physiological functions. Absence of these microgeometries in engineered tissues may undermine the effectiveness of corresponding tissue repair and regeneration. This paper introduces a novel approach to create tissue scaffolds with biomimetic 3D undulated microtopographies. The mechanical micromilling technology is used for precise and reproducible fabrication of poly(methyl methacrylate) (PMMA) master molds with 3D undulated microtopographies. Poly(dimethylsiloxane) (PDMS) production molds are then created using the master molds through elastomer molding. Next, gelatin-chondroitin-6-sulfate-hyaluronic acid (Gel-C6S-HA) is filled into the PDMS molds, lyophilized to obtain solid porous scaffolds, and covalently cross-linked to control biodegradability. The utility of the final porous scaffolds with undulated microtopographies mimicking dermal papillae of skin is demonstrated in vitro by culturing neonatal human fibroblasts (NHFs) on the scaffold surfaces for up to 7 days. The assessment of the mold and scaffold geometries demonstrates high accuracy and reproducibility of the PMMA mold fabrication, as well as well-controlled undulated microtopographies and porous microstructures of the final scaffolds. The analysis of cell responses to the undulated microtopographies shows the biocompatibility and effectiveness of the final scaffolds, as well as unique cellular response to these biomimetic topographies at the macroscopic level.