Frontiers in Neuroscience

Despite its high prevalence and socioeconomic costs, the condition of tinnitus shares with related neuropsychiatric disorders the characteristic that, to this day, it cannot be cured. Two contradictory views of the origin of tinnitus (peripheral hyperexcitability and central brain oscillation changes linked to prediction error) are currently discussed without any regard for one another. We now firstly used a compact 64 sensor optically pumped magnetometer (OPM)-MEG system to study a group of tinnitus subjects without co-morbidity of hyperacusis. This new technology provided an unprecedented opportunity for analyzing hemisphere-specific brain activity changes with high spatial resolution in response to pure-tones with a pitch within or outside the tinnitus frequency. We observed in tinnitus smaller ABR amplitudes (reflecting reduced cochlear output synchrony) linked with reduced alpha and enhanced gamma activity at rest (reflecting elevated excitement of intracortical circuits). In tinnitus, reduced alpha and enhanced gamma brain activity at rest were associated with reduced evoked alpha, beta, and gamma in response to pure-tones within tinnitus frequencies (reflecting low signal-to-noise ratios in auditory target regions). Furthermore, elevated gamma activity in key regions in the brain involved in attention control was observed in tinnitus subjects: hypergamma activity was seen in posterior/frontal regions, that -when hyperactive - are predicted to trigger excessive attention to irrelevant stimuli. Weakened cochlear output synchrony, possibly through lowering tonic inhibitory strength in the ascending auditory pathway, can thus reduce alpha activity (default-mode network) and unleash cortical regions that control attention to irrelevant stimuli -- tracing tinnitus to perception.

Purpose. To evaluate whether a Geographic Atrophy Morphology-based Mapping Algorithm (GAMMA) grid, informed by geographic atrophy (GA) lesion morphology, can accelerate functional progression detection compared with a conventional 10-2 grid and a dense grid (129 locations). This work is motivated by emerging regulatory expectations requiring at least five locations to worsen by [&ge;]7 dB from baseline. Methods. Binary atrophy masks from six autofluorescence images were used to simulate GA expansion over 3 years at 3-month intervals using a stochastic perimeter-growth model with a fixed preferential expansion direction (Pdir). For each image, 32 independent growth histories and 32 microperimetric test realisations per history were generated. For each grid (10-2, Dense, and GAMMA), 5-point clusters were selected outside the baseline GA lesion along three directions (0{degrees}, 30{degrees}, 120{degrees}) away from Pdir, simulating full, partial, and no prior knowledge of Pdir. Ground-truth sensitivities were <0 dB inside the GA lesion and normal outside, calculated using a published normative equation. Response variability was simulated following Henson et al. with baseline averaging. Detection time was the first visit at which all five selected locations showed [&ge;]7 dB loss from baseline. Survival curves and median detection times (T50) were used to compare grid performance. Results. The GAMMA grid achieved the earliest progression detection across all scenarios. Under full knowledge of the expansion direction, T50 was 1.0 year for GAMMA versus 1.25 and 1.5 years for Dense and 10-2, respectively. With partial knowledge, GAMMA's T50 was 1.25 years versus 1.5 and 2.0 years for Dense and 10-2. Even under no knowledge, GAMMA detected progression earliest (T50 = 1.5 years), while Dense required 6 months longer and 10-2 nearly double the time (2.75 years). Conclusions. The automatic GAMMA grid accelerates detection of localised functional progression compared with conventional and dense grids. Structure-informed grid optimisation may better align testing with likely expansion paths, potentially reducing follow-up duration and sample sizes in perimetry-based interventional trials.

Individuals with inherited retinal dystrophies (IRDs) undergo a slow, genetically heterogeneous loss of vision, yet how the visual cortex and non-visual sensory, motor, and psychological systems adapt to this deprivation remains poorly characterized. Existing evidence comes mainly from single-modality, cross-sectional studies that rarely account for genetic heterogeneity, making it hard to distinguish adaptive change from a direct, non-retinal mutation effect, since several IRD genes are not retina-specific. To address this gap, we designed an observational, longitudinal, multimodal protocol that combines ophthalmological, genetic, and in silico characterization with electrophysiological (steady-state visual evoked potentials and TMS-EEG), chemosensory, sensorimotor, and psycho-personological assessments. Patients aged 18 to 75 years with rod-cone (retinitis pigmentosa, Usher syndrome) or cone and cone-rod dystrophies will be assessed at baseline (T0) and at an 18-month follow-up (T1); sighted controls, matched for age, sex, and handedness, will complete the same battery once. Importantly, pairing genotypic with phenotypic data allows changes in non-visual domains to be interpreted against, rather than independently of, each patient's molecular background. We expect individuals with IRDs to differ from controls in visual cortical responsiveness and in selected non-visual sensory and sensorimotor measures, with genotype-related differences explored where sample size permits. Given the rarity of IRDs, the design is exploratory and emphasizes effect sizes and individual variability over large-sample inference. The protocol was approved by the Ethics Committee of the University of Verona (CARP 08.R1/2024) and follows the Declaration of Helsinki and the GDPR; findings will be disseminated through peer-reviewed publications and shared with patients and IRD patient associations.

In this study, we investigated the neural and behavioral basis of motivationally objective versus subjective value-based decisions. Using a within-subject fMRI design, healthy participants performed a risky decision-making task that elicited different levels of subjectivity in decision-making in two task conditions. In the Best or objective condition, choices were rewarded only when they were objectively best on a given trial, incentivizing decisions based on externally specified per-trial point maximization. In the Choice or subjective condition, participants received the reward associated with the chosen option, irrespective of how it compared to the unchosen option, allowing greater freedom to exercise subjective preferences in decision policy. Behaviorally, participants relied more on objectively optimal policy in the Best than the Choice condition. There was also a greater consensus across participants in behaviorally displayed and self-reported policies in the Best condition as well as a greater commitment to a single policy by individual participants in this condition, further confirming more objective behavior in the Best condition, compared to Choice. Moreover, behavioral inferences showed a greater agreement with self-report in the Best condition. Our fMRI results showed that the decision-making in Choice, relative to the Best condition, was associated with greater BOLD response in mid-cingulum/posterior cingulate cortex and dorsal anterior cingulate cortex, suggesting their involvement in less externally constrained, or motivationally subjective, decision-making.

Electroretinography (ERG) measures the functional response of distinct retinal cells to light, but was largely displaced by structural imaging in the 2000s. Standardization efforts by the International Society for Clinical Electrophysiology of Vision (ISCEV) began in the late 1980s, and collapsed the rich time-series traces into reproducible components and implicit times. Recent improvements in hardware (RETeval) and software (artificial intelligence) may increase the utility of ERG data. However, no ERG-specific foundation models exist, and there are not enough public datasets to train one. We asked whether time-series foundation models (FMs) trained without ERG-specific pre-training could be adapted through transfer learning. Using two public datasets, PERG-IOBA (pattern ERG with ocular diagnoses), and LEOPs (full-field ERG focusing on Autism Spectrum Disorder, ASD), we interrogated how FMs could improve over smaller within-domain models. We measured the binary (healthy/typically developing vs any annotation) and multiclass (specific family/diagnosis) classification performance of both frozen and fine-tuned FMs, alongside custom autoencoder and multiscale models, using patient-aware splits for cross validation. We benchmark the same architectures against PTB-XL, a large 12-lead ECG corpus, as both a control for each approach and to explore scaling behavior. We show that 1) pre-trained FMs can reconstruct masked traces from all three datasets, 2) frozen and fine-tuned embeddings, especially combined with multimodal metadata through masked autoencoders, performed best on classification tasks. Performance on PTB-XL was maintained down to 300 records, comparable in size to the ERG datasets. We could not reproduce published classification performance on the ASD task. Taken together, these results support general purpose foundation models as a practical approach to ERG analysis.

Deep brain stimulation (DBS) is transforming from a static therapy toward adaptive systems that adjust stimulation based on neural biomarkers. However, the detection of reliable biomarkers that capture the multi-dimensional nature of complex symptoms is often challenging. Here we demonstrate volitional DBS (vDBS)--a paradigm in which patients use brain-computer interface (BCI) training to learn self-regulation of a neural signal that then controls closed-loop DBS. Two patients with Parkinson's disease implanted with sensing-enabled neurostimulators completed chronic, at-home BCI training by playing an airplane simulation game. Through training, they were able to effectively down-regulate their cortical beta signal (p's < 1e-10), represented as the real-time position of a plane in the BCI game. Following training, this cortical beta signal served as the input to a closed-loop DBS algorithm. By modulating their beta signal to cross personalized thresholds, patients voluntarily increased or decreased neurostimulation amplitude at will, in the absence of physical movement (p's < 1e-10). This proof-of-principle demonstration establishes that volitional control of intracranial neurostimulation is achievable without the need of an externalized manual controller. BCI-vDBS could potentially be used for a range of neuropsychiatric conditions and brain rehabilitation to support personalized control of neurostimulation.

Despite our best efforts to stay focused on a task, our arousal waxes and wanes over time. Lower levels of arousal are typically associated with drowsiness, whereas higher levels are often associated with stress. These changes in arousal move us away from ideal task performance and manifest as fluctuations in neural activity. We asked whether moment-by-moment neurofeedback could be used to counteract neural fluctuations and thereby regulate arousal levels. Here, we developed an intracortical brain-computer interface (BCI) in which animals used visual neurofeedback to maintain neural population activity in prefrontal cortex near a pre-specified activity target. We found animals used moment-to-moment neurofeedback to reduce neural fluctuations on timescales of seconds to hundreds of milliseconds, and that arousal-related regulation of neural activity was associated with BCI use. Our findings suggest that neurofeedback may enhance or restore regulation of neural activity, with potential clinical applications in conditions where such regulation is impaired.

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject-wise cross-validation. Each experiment was repeated ten times. SCCNet obtained the highest mean subject-level accuracy, sensitivity, and F1 score, while ShallowConvNet achieved the highest mean segment-level accuracy, specificity, and precision. Subject-level aggregation improved mean accuracy for all evaluated models, and performance varied substantially across datasets and diagnostic tasks. Higher computational cost did not consistently correspond to better classification performance, with several compact architectures remaining competitive with substantially larger models. The results provide a reproducible reference for comparing EEG-based dementia classification models under consistent subject-independent evaluation conditions.

Difficulties understanding speech in noise remain a common complaint even among listeners with normal hearing sensitivity, highlighting the need for objective, more effective measures of real-world listening. The goal of this study was to validate the use of a novel, chirped-speech (Cheech) stimulus - continuous, naturally-spoken speech fused with chirps designed to elicit robust auditory evoked potentials - to characterize relationships between speech recognition, listening effort, and auditory neural encoding. Twenty-five normal-hearing adults completed a sentence-recognition task using both original (unmodified) and Cheech-modified AzBio sentence lists in quiet, +3 dB, and -3 dB signal-to-noise ratio (SNR) conditions while neural responses from the brainstem through cortex were recorded simultaneously. Speech recognition remained near ceiling in quiet but declined with decreasing SNR for both original and Cheech stimuli. Compared with clean speech, Cheech-modified speech showed slightly poorer recognition performance as SNR decreased and somewhat higher perceived effort overall. Yet, Cheech was highly effective at evoking auditory responses from the brainstem (auditory brainstem response, ABR) through the cortex (including middle- and late-latency responses, MLR and LLR) even with <5 minutes listening time per condition. Neural responses showed reduced amplitudes and prolonged latencies as SNR decreased. In general, ABR latencies and wave I amplitudes were associated with speech-in-noise recognition performance, whereas cortical responses (MLR Na, Nb, and LLR P1) were associated with subjective workload. These findings show that Cheech-modified speech preserves intelligibility while yielding robust, multilevel neural recordings during sentence perception, offering a promising approach to examine hierarchical auditory processing under ecologically relevant speech-in-noise conditions.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.

The availability of hearing aids that can connect wirelessly to smartphone technologies via Bluetooth has grown exponentially in recent years. However, there is limited evidence assessing the benefits of user-adjustability afforded by these devices. This study aimed to assess the benefits of smartphone-connected hearing aids and an accompanying application (or app) in new and existing hearing aid users. In this single-centre, prospective, observational study, 44 adult hearing aid users (14 new and 30 existing) were recruited. Participants were fitted bilaterally with smartphone-connected hearing aids that could be adjusted by the user via an app. Self-reported outcome measures were collected at fitting and after seven-weeks of using the device in everyday life. For both new and existing hearing aid users, significant improvements in social participation, hearing-related fatigue, and hearing aid benefit and satisfaction were found. For existing hearing aid users, all outcomes were significantly better for the smartphone-connected hearing aids plus app in comparison to their existing hearing aids that did not connect to a smartphone, all with moderate-to-large clinical effect sizes (d> .6). User-controllability via the app was identified as the key benefit, and most participants (68%) reported that the app met their needs 'extremely' or 'very well'. These results suggest that, when used in conjunction with an app, smartphone-connected hearing aids can improve hearing outcomes due to greater user-controllability to improve listening. Thus, smartphone-connected hearing aids have the potential to facilitate patient-centred care, empowering the individual to successfully manage their hearing loss.

BackgroundBrain regions integrate neural information arriving from several convergent projection sources. At the mesoscale level, neural projections can potentially span both hemispheres and extend along the entire rostrocaudal axis, which complicates efforts to map their full extent. To address this issue, we describe a novel method for mapping such mesoscale connectivity in vivo and ex vivo. Our neurotomographic approach utilizes micro-computed tomography (micro-CT) to image the spatial distribution of neural tracers bound to high Z-elements, e.g, gold. MethodsIn this study, we conjugated colloidal gold to a retrograde tracer wheat-germ agglutinin apo-horseradish peroxidase (WGA-HRP) and then stereotactically injected the gold-bound tracer (WAHG) into the mouse forebrain. Micro-CT was then used to image the brain in vivo and ex vivo, followed by three-dimensional reconstruction of tracer distribution. We then validated our approach by histologically processing the brains using silver enhancement to label gold particles; this enabled a direct comparison of histological labeling with the neurotomographic images. ResultsWe found that micro-CT imaging could reveal the major spatial distributions of the gold-bound tracer, which was consistent across in vivo and ex vivo imaging conditions. Moreover, the neurotomographically determined patterns corresponded with the labeling observed in histologically processed tissue, with the major sites of labeling reliably detected in reconstructed neurotomographic images. ConclusionsOverall, our findings demonstrate a potential novel method for non-destructive, three-dimensional mapping of neural tracers in vivo. This novel approach can potentially guide targeted multi-site recordings, enable validation of injection site placement, and facilitate rapid longitudinal connectomic analyses in vivo.

Objective: This study investigated whether plosive consonants carry a perceptual loudness weighting that significantly exceeds that of non-plosive consonants when judged by hearing-impaired listeners. Design: A prospective loudness matching experiment utilizing the method of adjustment. Study Sample: 19 consenting native English speakers (Mean age: 61.4, SD: 16.4) with bilateral mild to moderate high-frequency sensorineural hearing loss, indicative of presbycusis. Stimuli: 13 vowel-consonant-vowel (VCV) nonsense syllables, exclusively utilizing the flanking vowel /u/. Results: Descriptive analysis revealed a strong time-order effect influencing loudness judgments for 7 of the 13 VCV test stimuli. Statistical testing showed no significant didference (P = 0.94) between the relative amplitudes corresponding to the point of equal loudness for plosive-containing versus non-plosive-containing VCV stimuli. However, 6 individual VCV stimuli, containing consonants from 4 separate manners of articulation, produced significant loudness matching data (P < 0.01). Conclusions: The results falsify the hypothesis that plosives, analyzed collectively as a class, possess a heavier perceptual loudness weighting than non-plosive consonants. While 6 individual VCV stimuli indicated potential individual consonantal loudness weightings, these findings must be interpreted cautiously due to the restriction to a single vowel context and the presence of procedural time-order biases.

Purpose: To develop and evaluate an anatomy-aware deep learning framework for enhancement of neonatal 64mT T2-weighted MRI that improves anatomical visibility while preserving native ultra-low-field contrast and enabling quantitative structural analysis. Methods: A multitask network, jointly performing image enhancement and tissue segmentation, was trained on 75 and evaluated on 20 paired neonatal 64mT/3T MRI datasets spanning a broad range of gestational ages and pathologies. To preserve native 64mT contrast, 3T images were locally harmonized before training. The framework also generated quality-control maps and regional volumetric measurements. Volumetric agreement was further assessed in 40 paired term-born control datasets. Results: Enhanced 64mT images showed improved image quality metrics and better delineation of cortical, deep gray matter, ventricular, white matter, and posterior fossa structures while maintaining native contrast characteristics. Tissue segmentations demonstrated good agreement with reference 3T labels. Volumetric measurements showed excellent correspondence with 3T across major tissue compartments, with only small systematic regional biases. Conclusions: Anatomy-aware enhancement enables automated tissue segmentation and volumetric analysis directly from neonatal 64mT MRI while preserving native image contrast. These findings support the feasibility of quantitative neonatal neuroimaging at ultra-low field.

Intelligible speech disrupts selective auditory attention more than an unintelligible stream. However, low-level acoustic features of intelligible speech are relatively similar to target speech, confounding results. While controlling acoustic similarity and limiting energetic masking, we examined how masker intelligibility affects behavior and electroencephalography (EEG). Normal hearing listeners detected color words within a target stream of randomly timed words while ignoring an ongoing masker. Maskers were either spoken by the same or a different talker and comprised either isochronous sequences of intelligible words or temporally scrambled versions. Scrambled maskers either lacked broadband energy changes over time (Experiment 1) or were amplitude modulated to have the same energy profiles as intelligible, isochronous maskers (Experiment 2). In both experiments, scrambled maskers yielded better performance than intelligible maskers. For intelligible maskers, performance was better for different compared to identical talkers. EEG responses paralleled behavior: target-evoked onset responses were larger for scrambled than for intelligible maskers, particularly for identical talkers. Later target recognition responses were larger for color than other target words but unaffected by masker type or talker. Even when low-level acoustic features were carefully matched, intelligible maskers impaired auditory attention and reduced target-evoked neural responses more than scrambled maskers, implicating early sensory filtering.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Several types of cues contribute to speech recognition in multi-talker environments. In this study, we investigated how talker head-orientation related (THOR) cues and extended high- frequency (EHF; >8kHz) cues affect speech-in-speech recognition for both female and male speech. We examined the THOR benefit associated with a non-facing masker talker head orientation (relative to a facing orientation) as a function of masker talker facing angle. The target talker always faced the listener, whereas co-located maskers were tested with eight different masker head angles, ranging from 0{degrees} (facing the listener) to facing 180{degrees} away. Two filtering conditions were tested: full- band and low-pass filtered at 8 kHz. A THOR benefit was observed at masker head angles greater than 45{degrees}, increasing from 2 dB to 8 dB between angles of 67.5{degrees} and 180{degrees}. This benefit was reduced for low-pass filtered speech. Access to EHF cues improved performance, but only for masker head angles >22.5{degrees}. There was no significant relationship between 16-kHz pure-tone thresholds and performance for young, normal-hearing listeners with good EHF hearing. These findings indicate that listeners benefit from non-facing masker talker head orientations >45{degrees} when the target talker is facing the listener, with greater benefit for larger head angles.

PurposeThe neural representation of pitch and timbre in complex sounds has previously been studied using synthetic, controlled stimuli to investigate underlying encoding mechanisms. These studies provide information about how single attributes of sound are represented in the inferior colliculus (IC), a critical hub of the auditory pathway where neurons are sensitive to stimulus periodicity and spectral shape, giving rise to representations of pitch and timbre, respectively. However, there is a gap in understanding how natural sounds with both pitch and timbre attributes, such as instrument sounds, are represented in the IC. MethodsIn this study, extracellular recordings were made in the IC of awake rabbits in response to natural instrument stimuli varying in fundamental frequency (F0) to determine how instrument identity (timbre) and F0 (pitch) are represented in IC neurons. ResultsUsing decoding models for instrument identification, we found that instrument identity was redundantly encoded in a population of neurons with diverse rate and timing characteristics. F0 identification using decoding models trained on single-neuron rate responses was poor, but the population of rate responses contained enough information to identify F0 reliably. F0 information was also encoded in single-neuron temporal responses up to 196 Hz. F0 identification from a population of temporal responses was accurate up to approximately 900 Hz, but accuracy decreased at high F0s. For the task in which F0 was identified based on responses to both oboe and bassoon stimuli that had overlapping F0s, performance decreased compared to F0 identification based on responses to a single instrument. ConclusionThis result supports the hypothesis that pitch and timbre information are encoded jointly in the IC.

Wearable magnetoencephalography (MEG) enabled by optically pumped magnetometers (OPMs) promises improved comfort and motion tolerance. This is particularly beneficial when measuring brain activity in children who cannot sit still for long periods of time. Compared to cryogenic MEG, wearable MEG allows larger head movements, but they result in artifacts due to uncompensated background fields and reduce source localization accuracy. Spatial filtering methods can partially compensate these motion-induced artifacts, but they are most effective when used in combination with background field nulling. This is because accurate spatial filtering relies on an accurate estimate of the sensor gain and orientation of its sensitive axis. Through simulations, we first deduce the target residual background field that is necessary for accurate dipole localization (< 1 cm) in the presence of head movements. Using our open-source printed circuit board (PCB) coils, we develop a method to dynamically null the background field. We demonstrate that our dynamic field nulling method allows improved localization of somatosensory evoked fields (SEFs) by maintaining the background field below the target residual fields established in the simulations. Our study highlights the importance of tracking both the background field and the head position relative to the background field for quality assurance in wearable MEG.

Studies in humans and murine models have pointed towards a possible link between metabolic syndrome, which shows insulin resistance and metabolic dysregulation, and Alzheimer's disease (AD) pathology marked by amyloid-beta (A{beta}) accumulation and hypometabolism in the brain. Yet, the underlying biological mechanisms by which metabolic syndrome affects these pathological changes in AD brain remain unknown. We hypothesized that insulin resistance is responsible for alterations in blood-brain barrier (BBB) transport of A{beta} peptides and glucose. This hypothesis was tested by employing radiolabeled ligands (125I-A{beta}40, 125I-A{beta}42, and 18F-FDG) in high-fat diet (HFD)-fed mouse models that manifest metabolic syndrome. Further, we assessed alterations in the expression of various molecular mediators within the brain microcapillaries harvested from both low-fat diet (LFD)-fed and HFD-fed mice. Our findings show that HFD-fed mice developed peripheral insulin resistance and obesity. In addition, HFD-fed mice demonstrated an increase in the influx rate of A{beta} peptides and a reduction in 18F-FDG (a glucose surrogate) influx rate compared to LFD-fed mice. These transport changes are associated with the increase in the BBB endothelial expression of RAGE (receptor to traffic A{beta} from plasma-to-brain) and reduction of GLUT1 (glucose transporter) expression in HFD-fed mice compared to LFD-fed mice. Moreover, disruption in insulin signaling, as indicated by reduced pAKT and pERK expression, was observed in HFD-fed mice. Inhibiting AKT or ERK phosphorylation resulted in similar changes in A{beta} and glucose uptake in polarized BBB endothelial cell monolayers in vitro. These results indicate that high-fat diet induced metabolic syndrome may lead to BBB dysfunction, characterized by increased plasma-to-brain A{beta} trafficking and diminished glucose transport at the BBB, thereby aggravating the expression of AD pathological hallmarks.