Non-invasive neuroimaging has long faced the inherent trade-off between spatial and temporal resolution. Acoustoelectric brain imaging (ABI) holds promise to bridge this gap by combining the spatial precision of focused ultrasound (millimeter-level) with the temporal resolution of electroencephalography (EEG) signals (millisecond-level). However, its transcranial application remains fundamentally challenged by skull-induced wavefront aberration and attenuation. Here, we developed a full ABI system featuring a custom 128-element ultrasound phased array. Our system integrates developed algorithms for transcranial phase and amplitude correction, which were experimentally validated through an ex vivo human skull. We demonstrate that our system enables precise intracranial focus steering (lateral error ≤ 0.2 mm), accurate source localization (error ≤ 0.8 mm), and high-fidelity waveform reconstruction (correlation coefficient > 0.84). This work addresses the fundamental challenge of skull-induced imaging quality degradation in ABI, providing algorithmic and systemic foundations for advancing non-invasive neuroimaging techniques.

Region of interest (ROI)-based analyses are commonly employed in structural neuroimaging; however, practical guidance on defining the multiple-comparison test family and selecting appropriate error-control procedures remains limited. In this study, we combine a concise methodological review with a simulation-based twin-set framework using real MRI-derived ROI volumes to examine how the definition of the test family and the choice of the correction method jointly influence the minimum detectable effect size in ROI-wise analyses. T1-weighted MRI data from 110 participants were processed with FreeSurfer, and volumetric measures from 130 ROIs were grouped into anatomically defined frontal, temporal, parietal, and occipital families. Paired groups were constructed to minimize baseline differences, and controlled volume increases ranging from 1% to 20% were introduced into a single ROI, while all other regions remained unchanged. Statistical significance thresholds were then evaluated for global whole-brain versus lobe-wise test families using Bonferroni correction and false discovery rate (FDR) control with the Benjamini-Yekutieli procedure. On average, uncorrected significance was reached at a 5.7% volume increase. Global Bonferroni correction required approximately 13.0%, and lobe-wise Bonferroni correction required 11.6%. Under FDR-BY correction, the thresholds were 15.3% for the global family and 13.7% for lobe-wise families. Restricting the test family from the whole brain to anatomically constrained lobe-wise subsets reduced the minimum detectable effect size by approximately 1.5% under both correction frameworks, while preserving the relative ordering of ROI-level sensitivity. These findings suggest that in ROI-based structural MRI, statistical sensitivity may depend not only on the choice between family-wise error rate or FDR control but also on how the test family is defined. A pragmatic implication is that confirmatory analyses should predefine anatomically or functionally coherent ROI families and apply conservative multiplicity control within these families, whereas exploratory analyses may require broader families and more flexible inference with explicit acknowledgment of the associated error structure.

How does the brain organize and transmit information during naturalistic conditions? Using large-scale movie-watching fMRI data spanning 14 diverse clips, we revealed a degree-based dual architecture of the functional connectome under naturalistic stimuli. During movie watching, the brain consistently expresses a conserved network backbone characterized by persistently high node degree in temporal and occipital sensory cortices and parietal association regions, whereas anterior higher-order regions show relatively lower node degree and substantially greater variability across different clips. We further found that this backbone links brain network organizational patterns to stimulus features of the clips, particularly audiovisual features capturing human presence and social communication. Building on this conserved backbone, different movie clips recruit distinct sets of rich-club hubs, reflecting a flexible hub organization distributed across superior temporal gyrus, temporo-parieto-occipital junction, precuneus/posterior cingulate cortex, intraparietal sulcus, and visual motion-sensitive regions. These hubs serve as key integrative nodes whose connectivity statistically mediates the relationship between stimulus features and cross-network integration. The conserved backbone and rich-club organization were reproduced in an independent movie-watching dataset analyzed with an identical pipeline, providing partial validation of these topological findings. Together, these findings reveal a network-level organizing principle in which a conserved backbone supports stable large-scale coordination, while flexible hub organization enables feature-specific coupling between stimulus features and distributed brain networks.

Hemispheric EEG power asymmetry is most often studied as frontal alpha asymmetry (FAA), but evidence suggests that asymmetry effects may be distributed across scalp regions and frequency bands, particularly in PTSD. We introduce the Dynamic EEG Power Differential (DEPD) index to quantify whole-scalp, multi-band, inter- and intra-hemispheric power differentials using time-resolved spectral estimates. Resting-state, eyes-closed EEG (32-channel) was recorded for 3 min in individuals with PTSD (n = 10) and healthy controls (n = 10). DEPD was computed as log power differences for all inter-hemispheric and intra-hemispheric electrode pairings across eight frequency bands (spanning delta to gamma) using both absolute and relative power. Group differences were tested nonparametrically with multiple-comparison control; within PTSD, DEPD features were related to symptom severity (PCL-5 scores). Homologous inter-hemispheric effects analogous to conventional asymmetry metrics were observed but did not predominate. The most prominent group- and severity-related effects were non-homologous inter- and intra-hemispheric differentials spanning anterior-posterior and fronto-parietal/fronto-occipital relationships and were strongly frequency-dependent. DEPD features derived from absolute versus relative power showed minimal overlap. Hemispheric power imbalance in controls and PTSD appears distributed and frequency-specific, and differences between PTSD and controls, as well as association with PTSD symptom severity, is better captured by these distributed inter- and intra-hemispheric power differentials at multitude frequency bands, compared with homologues asymmetries (particularly alpha band). DEPD provides a scalable framework for extracting distributed, frequency-resolved hemispheric power-differential features from resting EEG and may support future identification of candidate EEG markers in adequately powered validation studies.

This paper examines associations of atrophy in the amygdala, entorhinal cortex and hippocampus based on magnetic resonance imaging (MRI) and Positron Emission Tomography (PET) scans from two independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study. The amygdala, entorhinal cortex (ERC) and transentorhinal cortex (TEC) are shown to change earlier in the disease than the hippocampus based on volume atrophy rate. Over four hundred laminar reconstructions showed that ERC/TEC volume loss is linked to cortical thinning, as a more specific measure historically linked to the layer-specific pattern of tau pathology deposition. Additionally, high-field atlasing with delineations of amygdala subregions shows predominant volume loss in medial subregions including basomedial, basolateral, and corticocentromedial amygdala compared with the lateral subregion. Consistent with prior studies linking MRI-derived atrophy to hyperphosphorylated tau deposition in the amygdala and ERC/TEC, the atrophy rate marker demonstrates strong associations with tau PET measures and spatial co-localization with tau pathology.

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate neurophysiology of the human brain and treat brain disorders. Traditionally, therapeutic TMS has been applied in a one-size-fits-all approach, disregarding inter- and intraindividual differences. Brain state-dependent EEG-TMS, such as coupling TMS with a pre-specified phase of the sensorimotor μ-rhythm, enables the induction of differential neuroplastic effects depending on the targeted phase. But this approach is still user-dependent as it requires defining an a-priori target phase. Here, we present a first realization of a machine-learning-based, closed-loop real-time EEG-TMS setup to identify user-independently the individual μ-rhythm phase associated with high- vs. low-corticospinal excitability states. We applied EEG-TMS to 25 participants targeting the supplementary motor area- primary motor cortex network and used a reinforcement learning algorithm to identify the μ- rhythm phase associated with high- vs. low corticospinal excitability. We employed linear mixed effects models and Bayesian analysis to assess effects of reinforced learning on corticospinal excitability indexed by motor evoked potential amplitude, and functional connectivity indexed by the imaginary part of resting-state EEG coherence. Our results show that reinforcement learning identified the μ-rhythm phase associated with high- vs. low-excitability states, and their repetitive stimulation resulted in long-term increases vs. decreases in functional connectivity in the stimulated sensorimotor network. Our work demonstrated the feasibility of a reinforcement learning-based adaptive closed-loop EEG-TMS approach in a full experimental cohort, enabling user-independent identification of μ-rhythm phases associated with corticospinal excitability states.

Older adults with hearing loss often struggle to understand speech in background noise because degraded auditory input increases demand on attention, working memory, and control. Auditory-cognitive training (ACT) can improve speech-in-noise (SiN) comprehension, but whether such gains are accompanied by changes in large-scale neural connectivity remains poorly understood. We tested whether ACT was associated with pre-to-post changes in source-space alpha-band phase synchrony within an auditory-control network during continuous SiN comprehension, and whether such changes related to ACT-associated behavioural gains. Fifty-four native German-speaking older adults with mild-to-moderately severe sensorineural hearing loss completed either ACT or an active control intervention and underwent electroencephalography while listening to continuous speech in no-noise, low-noise (+8 dB signal-to-noise ratio, SNR), and high-noise (+3 dB SNR) conditions. We quantified alpha-band phase synchrony (8-12 Hz) between a priori pathways linking auditory-related/posterior temporal, inferior frontal, dorsolateral prefrontal, superior parietal, and dorsal anterior cingulate regions. In an exploratory pathway-wise screen, ACT was associated with pre-to-post changes in alpha-band phase synchrony in two left-lateralized pathways under no-noise and one left- and one right-lateralized pathway under low-noise, with no corresponding high-noise changes. Among the pathways identified in this screen, stronger post-training alpha-band phase synchrony in cingulate-prefrontal and parietal-prefrontal pathways was associated with better SiN comprehension accuracy under noisy listening conditions. These findings indicate that behavioural improvement was accompanied by condition-specific changes in alpha-band connectivity. They suggest a link between stronger post-training connectivity in control-related pathways and better SIN comprehension, while the pathway-specific pattern remains exploratory pending larger, preregistered studies.

The glymphatic system (GS) describes how the brain clears toxins and metabolic waste. Accumulation of these elements can contribute to onset and severity of brain disorders, so the importance of measuring the GS is vital to understanding how neurological disease may occur and potentially lead to developing preventative treatments. A simple technique referred to as Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS) was developed as a non-invasive MRI marker of GS functioning. However, various aspects of its measurement and putative interpretation have led to critiques of the technique and discussion focused upon whether it is truly a measure of GS. We located 393 DTI-ALPS articles with an exponential growth during the past few years. The most studied conditions where this technique has been employed was neurodegenerative diseases, followed by many other conditions such as sleep disorders, epilepsy, stroke, and mood disturbances. While most studies found a significant association with DTI-ALPS and their cohorts, there has been significant discussion related to the interpretation of the DTI-ALPS index and the mechanisms underlying changes in this value. The majority of studies found significant associations between DTI-ALPS and disease severity, although issues related to differences in methodologies are important to consider. Finally, the methods employed to generate the DTI-ALPS measurements as well as the variation in these approaches are summarized. DTI-ALPS is mathematically governed by radial diffusivity asymmetry and axonal geometry, and it serves as a highly sensitive but non-specific marker of white matter microstructural damage and neuroinflammation. Retrospectively applicable and highly reproducible, DTI-ALPS offers substantial translational potential for low-cost, large-scale clinical screening and longitudinal trial stratification when paired with other biomarkers.

The alpha band (8-13 Hz) is widely used as a personalized stimulation frequency in neuromodulation techniques, such as transcranial electrical or magnetic stimulation, targeting the prefrontal cortex. A critical step in these interventions is the identification of individualized stimulation frequencies, which are typically derived from endogenous alpha rhythms observed during resting-state brain activity. However, several aspects of this approach remain unresolved. To address these gaps, we collected resting-state EEG data under both eyes-open (EO) and eyes-closed (EC) conditions from 67 healthy individuals, along with 238 sessions of longitudinal recordings (scheduled monthly) from 21 individuals (averaging 11.3 sessions per participant over up to 16 months; mean duration: 10.3 months). Using multiple estimation methods, we systematically examined the frequency characteristics of the alpha band. Our analyses revealed that alpha peak estimation was unstable under EO conditions but robust under EC conditions. Furthermore, in participants with a prominent alpha peak, the frequencies estimated across different methods were highly consistent. Finally, we confirmed that both the frequency and power of alpha oscillations remain remarkably stable over extended periods. These findings provide crucial insights for optimizing personalized stimulation frequencies in neuromodulation and may contribute to the development of more targeted and effective interventions aimed at enhancing cognitive function.

This editorial introduces a special issue detailing the rapid evolution of multimodal neuroimaging biomarkers for Parkinson's disease (PD) and related movement disorders. Advances in MRI, including quantitative susceptibility mapping for iron dysregulation, neuromelanin-sensitive MRI, diffusion tensor imaging for free water quantification, metabolic spectroscopy, and functional network mapping, are reviewed. The integrated application of these modalities demonstrates that PD neurodegeneration cannot be captured by a single biomarker. Instead, these complementary tools define overlapping biological dimensions of the disease, spanning dopaminergic neuronal integrity, microstructural injury, glymphatic impairment, vascular abnormalities, and large-scale network reorganization. Furthermore, the integration of artificial intelligence and machine learning allows for the extraction of multidimensional patterns, improving the differential diagnosis between PD and atypical parkinsonian syndromes like multiple system atrophy and progressive supranuclear palsy. We conclude that while these multimodal biomarkers offer unprecedented mechanistic insights into disease progression and therapeutic response, their ultimate value depends on successful clinical translation. Moving forward, it would be essential for the field to prioritize standardized acquisition protocols, automated post-processing pipelines, and accessible reporting frameworks to implement these powerful research tools into routine clinical environments.