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.

Transcriptomic and proteomic profiling of bi-partite and tri-partite murine iPSC-derived neurospheroids under steady-state and inflammatory condition.

induced-pluripotent stem cell (iPSC)-derived neurospheroid (NSPH) models are an emerging in vitro toolkit to study the influence of inflammatory triggers on neurodegeneration and repair in a 3D neural environment. In contrast to their human counterpart, the absence of murine iPSC-derived NSPHs for profound characterisation and validation studies is a major experimental research gap, even though they offer the only possibility to truly compare or validate in vitro NSPH responses with in vivo brain responses. To contribute to these developments, we here describe the generation and characterisation of 5-week-old CX3CR1eGFP+/- CCR2RFP+/- murine (m)iPSC-derived bi-partite (neurons + astrocytes) and tri-partite (neurons + astrocytes + microglia) NSPH models that can be subjected to cellular activation following pro-inflammatory stimulation. First, cytokine analysis demonstrates that both bi-partite and tri-partite NSPHs can be triggered to release IL6 and CXCL10 following three days of stimulation with, respectively, TNFα + IL1ß + IFNγ and LPS + IFNγ. Additionally, immunocytochemical analysis for G3BP1 and PABPC1 revealed the development of stress granules in both bi-partite and tri-partite NSPHs after 3 days of stimulation. To further investigate the observed signs of inflammatory response and cellular stress, we performed an untargeted transcriptomic and proteomic analysis of bi- and tri-partite NSPHs under steady-state and inflammatory conditions. Here, using the combined differential gene and protein expression profiles between unstimulated and stimulated NSPHs, Ingenuity Pathway Analysis (IPA) confirms the activation of canonical pathways associated with inflammation and cellular stress in both bi-partite and tri-partite NSPHs. Moreover, our multi-omics analysis suggests a higher level of downstream inflammatory responses, impairment of homeostatic and developmental processes, as well as activation of cell death processes in stimulated tri-partite NSPHs compared to bi-partite NSPHs. Concluding, these results emphasise the advantages of including microglia in NSPH research to study inflammation-induced neurodegeneration in a 3D neural environment.

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.

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.

Longitudinal alterations in brain perfusion and vascular reactivity in the zQ175DN mouse model of Huntington's disease.

BACKGROUND: Huntington's disease (HD) is marked by a CAG-repeat expansion in the huntingtin gene that causes neuronal dysfunction and loss, affecting mainly the striatum and the cortex. Alterations in the neurovascular coupling system have been shown to lead to dysregulated energy supply to brain regions in several neurological diseases, including HD, which could potentially trigger the process of neurodegeneration. In particular, it has been observed in cross-sectional human HD studies that vascular alterations are associated to impaired cerebral blood flow (CBF). To assess whether whole-brain changes in CBF are present and follow a pattern of progression, we investigated both resting-state brain perfusion and vascular reactivity longitudinally in the zQ175DN mouse model of HD. METHODS: Using pseudo-continuous arterial spin labelling (pCASL) MRI in the zQ175DN model of HD and age-matched wild-type (WT) mice, we assessed whole-brain, resting-state perfusion at 3, 6 and 9 and 13 months of age, and assessed hypercapnia-induced cerebrovascular reactivity (CVR), at 4.5, 6, 9 and 15 months of age. RESULTS: We found increased perfusion in cortical regions of zQ175DN HET mice at 3 months of age, and a reduction of this anomaly at 6 and 9 months, ages at which behavioural deficits have been reported. On the other hand, under hypercapnia, CBF was reduced in zQ175DN HET mice as compared to the WT: for multiple brain regions at 6 months of age, for only somatosensory and retrosplenial cortices at 9 months of age, and brain-wide by 15 months. CVR impairments in cortical regions, the thalamus and globus pallidus were observed in zQ175DN HET mice at 9 months, with whole brain reactivity diminished at 15 months of age. Interestingly, blood vessel density was increased in the motor cortex at 3 months, while average vessel length was reduced in the lateral portion of the caudate putamen at 6 months of age. CONCLUSION: Our findings reveal early cortical resting-state hyperperfusion and impaired CVR at ages that present motor anomalies in this HD model, suggesting that further characterization of brain perfusion alterations in animal models is warranted as a potential therapeutic target in HD.

Postmortem imaging of fetuses at early gestations: A comparison of microfocus computed tomography with postmortem magnetic resonance at 9.4 T and postmortem ultrasound.

OBJECTIVE: To compare the diagnostic performance of postmortem ultrasound (PMUS), 9.4 T magnetic resonance imaging (MRI) and microfocus computed tomography (micro-CT) for the examination of early gestation fetuses. METHOD: Eight unselected fetuses (10-15 weeks gestational age) underwent at least 2 of the 3 listed imaging examinations. Six fetuses underwent 9.4 T MRI, four underwent micro-CT and six underwent PMUS. All operators were blinded to clinical history. All imaging was reported according to a prespecified template assessing 36 anatomical structures, later grouped into five regions: brain, thorax, heart, abdomen and genito-urinary. RESULTS: More anatomical structures were seen on 9.4 T MRI and micro-CT than with PMUS, with a combined frequency of identified structures of 91.9% and 69.7% versus 54.5% and 59.6 (p < 0.001; p < 0.05) respectively according to comparison groups. In comparison with 9.4 T MRI, more structures were seen on micro-CT (90.2% vs. 83.3%, p < 0.05). Anatomical structures were described as abnormal on PMUS in 2.7%, 9.4 T MRI in 6.1% and micro-CT 7.7% of all structures observed. However, the accuracy test could not be calculated because conventional autopsy was performed on 6 fetuses of that only one structure was abnormal. CONCLUSION: Micro-CT appears to offer the greatest potential as an imaging adjunct or non-invasive alternative for conventional autopsies in early gestation fetuses.

Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington's disease.

Huntington's disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities. Multiple studies have found white matter anomalies in HD-affected humans and animal models of HD. The identification of sensitive white-matter-based biomarkers in HD animal models will be important in understanding disease mechanisms and testing the efficacy of therapeutic interventions. Here we investigated the progression of white matter deficits in the knock-in zQ175DN heterozygous (HET) mouse model of HD at 3, 6 and 11 months of age (M), reflecting different states of phenotypic progression. We compared findings from traditional diffusion tensor imaging (DTI) and advanced fixel-based analysis (FBA) diffusion metrics for their sensitivity in detecting white matter anomalies in the striatum, motor cortex, and segments of the corpus callosum. FBA metrics revealed progressive and widespread reductions of fiber cross-section and fiber density in myelinated bundles of HET mice. The corpus callosum genu was the most affected structure in HET mice at 6 and 11 M based on the DTI and FBA metrics, while the striatum showed the earliest progressive differences starting at 3 M based on the FBA metrics. Overall, FBA metrics detected earlier and more prominent alterations in myelinated fiber bundles compared to the DTI metrics. Luxol fast blue staining showed no loss in myelin density, indicating that diffusion anomalies could not be explained by myelin reduction but diffusion anomalies in HET mice were accompanied by increased levels of neurofilament light chain protein at 11 M. Altogether, our findings reveal progressive alterations in myelinated fiber bundles that can be measured using diffusion MRI, representing a candidate noninvasive imaging biomarker to study phenotype progression and the efficacy of therapeutic interventions in zQ175DN mice. Moreover, our study exposed higher sensitivity of FBA than DTI metrics, suggesting a potential benefit of adopting these advanced metrics in other contexts, including biomarker development in humans.

A preparation pulse for fast steady state approach in Actual Flip angle Imaging.

BACKGROUND: Actual Flip angle Imaging (AFI) is a sequence used for B1 mapping, also embedded in the Variable flip angle with AFI for simultaneous estimation of T1 , B1 and equilibrium magnetization. PURPOSE: To investigate the design of a preparation module for AFI to allow a fast approach to steady state (SS) without requiring the use of dummy acquisitions. METHODS: The features of a preparation module with a B1 insensitive adiabatic pulse, spoiler gradients, and a recovery time T r e c $T_{rec}$ were studied with simulations and validated via experiments and acquired with different k-space traveling strategies. The robustness of the flip angle of the preparation pulse on the acquired signal is studied. RESULTS: When a 90° adiabatic pulse is used, the forthcoming T r e c $T_{rec}$ can be expressed as a function of repetition times and AFI flip angle only as TR 1 ( n + cos α ) / ( 1 - cos 2 α ) $\mathrm{TR_1}(n+\cos \alpha )/(1-\cos ^2\alpha )$ , where n represents the ratio between the two repetition times of AFI. The robustness of the method is demonstrated by showing that using the values further away from 90° still allows for a faster approach to SS than the use of dummy pulses. CONCLUSIONS: The preparation module is particularly advantageous for low flip angles, as well as for AFI sequences that sample the center of the k-space early in the sequence, such as centric ordering acquisitions, and for ultrafast EPI-based AFI methods, thus allowing to reduce scanner overhead time.

Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents.

Neurodevelopmental disorders (NDDs) are a group of complex neurologic and psychiatric disorders. Functional and molecular imaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), can be used to measure network activity noninvasively and longitudinally during maturation in both humans and rodent models. Here, we review the current knowledge on rs-fMRI and PET biomarkers in the study of normal and abnormal neurodevelopment, including intellectual disability (ID; with/without epilepsy), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), in humans and rodent models from birth until adulthood, and evaluate the cross-species translational value of the imaging biomarkers. To date, only a few isolated studies have used rs-fMRI or PET to study (abnormal) neurodevelopment in rodents during infancy, the critical period of neurodevelopment. Further work to explore the feasibility of performing functional imaging studies in infant rodent models is essential, as rs-fMRI and PET imaging in transgenic rodent models of NDDs are powerful techniques for studying disease pathogenesis, developing noninvasive preclinical imaging biomarkers of neurodevelopmental dysfunction, and evaluating treatment-response in disease-specific models.

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.

Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder caused by expanded (≥ 40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and clinical signs and symptoms of the disease are better known, changes in the functional architecture of the brain, especially before the clinical expression becomes apparent, are not fully and consistently characterized. In this study, we sought to uncover functional changes in the brain in the heterozygous (HET) zQ175 delta-neo (DN) mouse model at 3, 6, and 10 months of age, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that worsen through age. Motor function disturbances are manifested in this model at 6 and 10 months of age. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the 10-months time point; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three ages and became progressively more pronounced at 6-, and 10 months of age. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three ages with 80% classification accuracy at 6 and 10 months of age.

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.

Resting-state fMRI reveals longitudinal alterations in brain network connectivity in the zQ175DN mouse model of Huntington's disease.

Huntington's disease is an autosomal, dominantly inherited neurodegenerative disease caused by an expansion of the CAG repeats in exon 1 of the huntingtin gene. Neuronal degeneration and dysfunction that precedes regional atrophy result in the impairment of striatal and cortical circuits that affect the brain's large-scale network functionality. However, the evolution of these disease-driven, large-scale connectivity alterations is still poorly understood. Here we used resting-state fMRI to investigate functional connectivity changes in a mouse model of Huntington's disease in several relevant brain networks and how they are affected at different ages that follow a disease-like phenotypic progression. Towards this, we used the heterozygous (HET) form of the zQ175DN Huntington's disease mouse model that recapitulates aspects of human disease pathology. Seed- and Region-based analyses were performed at different ages, on 3-, 6-, 10-, and 12-month-old HET and age-matched wild-type mice. Our results demonstrate decreased connectivity starting at 6 months of age, most prominently in regions such as the retrosplenial and cingulate cortices, pertaining to the default mode-like network and auditory and visual cortices, part of the associative cortical network. At 12 months, we observe a shift towards decreased connectivity in regions such as the somatosensory cortices, pertaining to the lateral cortical network, and the caudate putamen, a constituent of the subcortical network. Moreover, we assessed the impact of distinct Huntington's Disease-like pathology of the zQ175DN HET mice on age-dependent connectivity between different brain regions and networks where we demonstrate that connectivity strength follows a non-linear, inverted U-shape pattern, a well-known phenomenon of development and normal aging. Conversely, the neuropathologically driven alteration of connectivity, especially in the default mode and associative cortical networks, showed diminished age-dependent evolution of functional connectivity. These findings reveal that in this Huntington's disease model, altered connectivity starts with cortical network aberrations which precede striatal connectivity changes, that appear only at a later age. Taken together, these results suggest that the age-dependent cortical network dysfunction seen in rodents could represent a relevant pathological process in Huntington's disease progression.

ADEPT: Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs.

PURPOSE: To introduce a novel imaging and parameter estimation framework for accurate multi-shot diffusion MRI. THEORY AND METHODS: We propose a new framework called ADEPT (Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs) that enables fast diffusion MRI by allowing diffusion contrast settings to change between shots in a multi-shot EPI acquisition (i.e., intra-scan modulation). The framework estimates diffusion parameter maps directly from the acquired intra-scan modulated k-space data, while simultaneously accounting for shot-to-shot phase inconsistencies. The performance of the estimation framework is evaluated using Monte Carlo simulation studies and in-vivo experiments and compared to that of reference methods that rely on parallel imaging for shot-to-shot phase correction. RESULTS: Simulation and real-data experiments show that ADEPT yields more accurate and more precise estimates of the diffusion metrics in multi-shot EPI data in comparison with the reference methods. CONCLUSION: ADEPT allows fast multi-shot EPI diffusion MRI without significantly degrading the accuracy and precision of the estimated diffusion maps.

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.

Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping.

Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method's performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.

Unraveling the Role of Thyroid Hormones in Seasonal Neuroplasticity in European Starlings (Sturnus vulgaris).

Thyroid hormones clearly play a role in the seasonal regulation of reproduction, but any role they might play in song behavior and the associated seasonal neuroplasticity in songbirds remains to be elucidated. To pursue this question, we first established seasonal patterns in the expression of thyroid hormone regulating genes in male European starlings employing in situ hybridization methods. Thyroid hormone transporter LAT1 expression in the song nucleus HVC was elevated during the photosensitive phase, pointing toward an active role of thyroid hormones during this window of possible neuroplasticity. In contrast, DIO3 expression was high in HVC during the photostimulated phase, limiting the possible effect of thyroid hormones to maintain song stability during the breeding season. Next, we studied the effect of hypothyroidism on song behavior and neuroplasticity using in vivo MRI. Both under natural conditions as with methimazole treatment, circulating thyroid hormone levels decreased during the photosensitive period, which coincided with the onset of neuroplasticity. This inverse relationship between thyroid hormones and neuroplasticity was further demonstrated by the negative correlation between plasma T3 and the microstructural changes in several song control nuclei and cerebellum. Furthermore, maintaining hypothyroidism during the photostimulated period inhibited the increase in testosterone, confirming the role of thyroid hormones in activating the hypothalamic-pituitary-gonadal (HPG) axis. The lack of high testosterone levels influenced the song behavior of hypothyroid starlings, while the lack of high plasma T4 during photostimulation affected the myelination of several tracts. Potentially, a global reduction of circulating thyroid hormones during the photosensitive period is necessary to lift the brake on neuroplasticity imposed by the photorefractory period, whereas local fine-tuning of thyroid hormone concentrations through LAT1 could activate underlying neuroplasticity mechanisms. Whereas, an increase in circulating T4 during the photostimulated period potentially influences the myelination of several white matter tracts, which stabilizes the neuroplastic changes. Given the complexity of thyroid hormone effects, this study is a steppingstone to disentangle the influence of thyroid hormones on seasonal neuroplasticity.

Uncovering a 'sensitive window' of multisensory and motor neuroplasticity in the cerebrum and cerebellum of male and female starlings.

Traditionally, research unraveling seasonal neuroplasticity in songbirds has focused on the male song control system and testosterone. We longitudinally monitored the song behavior and neuroplasticity in male and female starlings during multiple photoperiods using Diffusion Tensor and Fixel-Based techniques. These exploratory data-driven whole-brain methods resulted in a population-based tractogram confirming microstructural sexual dimorphisms in the song control system. Furthermore, male brains showed hemispheric asymmetries in the pallium, whereas females had higher interhemispheric connectivity, which could not be attributed to brain size differences. Only females with large brains sing but differ from males in their song behavior by showing involvement of the hippocampus. Both sexes experienced multisensory neuroplasticity in the song control, auditory and visual system, and cerebellum, mainly during the photosensitive period. This period with low gonadal hormone levels might represent a 'sensitive window' during which different sensory and motor systems in the cerebrum and cerebellum can be seasonally re-shaped in both sexes.

Long-term ovarian hormone deprivation alters functional connectivity, brain neurochemical profile and white matter integrity in the Tg2576 amyloid mouse model of Alzheimer's disease.

Premenopausal bilateral ovariectomy is considered to be one of the risk factors of Alzheimer's disease (AD). However, the underlying mechanisms remain unclear. Here, we aimed to investigate long-term neurological consequences of ovariectomy in a rodent AD model, TG2576 (TG), and wild-type mice (WT) that underwent an ovariectomy or sham-operation, using in vivo MRI biomarkers. An increase in osmoregulation and energy metabolism biomarkers in the hypothalamus, a decrease in white matter integrity, and a decrease in the resting-state functional connectivity was observed in ovariectomized TG mice compared to sham-operated TG mice. In addition, we observed an increase in functional connectivity in ovariectomized WT mice compared to sham-operated WT mice. Furthermore, genotype (TG vs. WT) effects on imaging markers and GFAP immunoreactivity levels were observed, but there was no effect of interaction (Genotype × Surgery) on amyloid-beta-and GFAP immunoreactivity levels. Taken together, our results indicated that both genotype and ovariectomy alters imaging biomarkers associated with AD.