Various dietary patterns (DPs) may benefit or harm cognitive status through their components. Publications assessing the impact of DPs on cognitive scores using neuropsychological tests have often led to less promising results. Recently, numerous meta-analyses and systematic reviews have utilized neuroimaging to identify more subtle brain-associated alterations related to cognition. Combining neuroimaging methods with neuropsychological assessments could clarify these findings. This umbrella review was conducted to systematically explore evidence on the impact of DPs on neuroimaging biomarkers in older adults with cognitive disorders. Scientific databases, including Scopus, PubMed, and Web of Science, were comprehensively searched from the earliest available data until May 11, 2024. Out of 89 papers, 15 meta-analyses and systematic reviews were included in our umbrella review. These selected papers addressed 27 DPs and their impact on neuroimaging biomarkers. Most selected papers were of moderate quality. Studies revealed that greater adherence to the Mediterranean diet (MedDiet) correlated with increased cortical thickness, improved glucose metabolism in the brain, and reduced amyloid-beta and tau deposition, as evidenced by magnetic resonance imaging and other neuroimaging techniques. Higher adherence to healthy DPs, such as the MedDiet, reduced the risk of Alzheimer's disease and mild cognitive impairment. In contrast, Western and high glycemic diets were associated with increased cognitive decline.

Electroencephalography (EEG) is widely used for assessing infant's brain activity, and multi-channel recordings support studies on cortical activity networks. Here, we aimed to assess how the number of recording electrodes affect the quality and level of details accessible in studying infant's cortical networks.

The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.

The left lateralization of language has been attributed to hemispheric specialization for processing rapidly changing information. While interhemispheric differences in auditory cortex organization support this view, the macrostructure of the entire cerebral cortex has not been thoroughly examined from this perspective. This study investigated hemispheric asymmetries in cortical surface area and thickness and their relationship to pronunciation scores from oral reading using the Human Connectome Project Young Adult dataset (N=1113). Heschl's gyrus had the most left-lateralized surface area, while the temporal pole showed the strongest right-lateralization in thickness. These areas correspond to the core components of speech: sound and meaning. Notably, their structural features were the only ones also yielding a significant correlation with pronunciation scores. Additionally, Broca's area's posterior region (pars opercularis), involved in articulatory phonological processing, showed leftward lateralization, contrasting with the right-lateralized anterior portions. Left-hemisphere language areas were largely thinner and more extended than their right-sided homologs with a larger white-to-gray matter ratio. Cortical thickness was inversely related to surface area. The lateralization of auditory-related language areas and their structure's correlation with pronunciation in oral reading supports a genetically based auditory foundation for language. A thinner, more efficient cortex with larger surface areas and increased myelination likely underlies the left-hemispheric dominance of language. Thinner, more extended brain areas have been linked to more myelination and wider cortical columns and intercolumnar space. This provides the potential for a fast network of interconnected, discrete information units able to support language's demands of rapid categorical processing.

Understanding the neural mechanism of sensorimotor adaptation is essential to reveal how the brain learns from errors, a process driven by sensory prediction errors. While the previous literature has focused on cortical and cerebellar changes, the involvement of the thalamus has received less attention. This functional magnetic resonance imaging study aims to explore the neural substrates of learning from sensory prediction errors with an additional focus on the thalamus. Thirty participants adapted their goal-directed reaches to visual feedback rotations introduced in a step-wise manner, while reporting their predicted visual consequences of their movements intermittently. We found that adaptation initially engaged the cerebellum and fronto-parietal cortical regions, which persisted as adaptation progressed. By the end of adaptation, additional regions within the fronto-parietal cortex and medial pulvinar of the thalamus were recruited. Another finding was the involvement of bilateral medial dorsal nuclei, which showed a positive correlation with the level of motor adaptation. Notably, the gradual shift in the predicted hand movement consequences was associated with activity in the cerebellum, motor cortex and thalamus (ventral lateral, medial dorsal, and medial pulvinar). Our study presents clear evidence for an involvement of the thalamus, both classical 'motor' and higher-order nuclei, in error-based motor learning.

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is crucial for early treatment interventions. However, the heterogeneity of MCI data samples poses a challenge for CN vs. MCI vs. AD multiclass classification, as some samples are closer to AD while others are closer to CN in the feature space. Previous attempts to address this challenge produced inaccurate results, leading most frameworks to break the assessment into binary classification tasks such as AD vs. CN, AD vs. MCI, and CN vs. MCI. Other methods proposed sequential binary classifications such as CN vs. others and dividing others into AD vs. MCI. While those approaches may have yielded encouraging results, the sequential binary classification method makes interpretation and comparison with other frameworks challenging and subjective. Those frameworks exhibited varying accuracy scores for different binary tasks, making it unclear how to compare the model performance with other direct multiclass methods. Therefore, we introduce a classification framework comprising unsupervised ensemble manifold regularized sparse low-rank approximation and regularized multikernel support vector machine (SVM). This framework first extracts a joint feature embedding from MRI and PET neuroimaging features, which were then combined with the Apoe4, Adas11, MPACC digits, and Intracranial volume features using a regularized multikernel SVM. Using that framework, we achieved a state-of-the-art (SOTA) result in a CN vs. MCI vs. AD multiclass classification (mean accuracy: 84.87±6.09, F1 score: 84.83±6.12 vs 67.69). The methods generalize well to binary classification tasks, achieving SOTA results in all but the CN vs. MCI category, which was slightly lower than the best score by just 0.2%.

Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg is a robust deep learning model designed for automatic brain segmentation across various contrasts and resolutions. This study validates the SynthSeg robust brain segmentation model on computed tomography (CT), using a multi-center dataset.

The ongoing brain activity serves as a baseline that supports both internal and external cognitive processes. However, its precise nature remains unclear. Considering that people display various patterns of brain activity even when engaging in the same task, it is reasonable to believe that individuals possess their unique brain baseline pattern. Using spatial independent component analysis on a large sample of fMRI data from the Human Connectome Project (HCP), we found an individual-specific component which can be consistently extracted from either resting-state or different task states and is reliable over months. Compared to functional connectome fingerprinting, it is much more stable across different fMRI modalities. Its stability is closely related to high explained variance and is minimally influenced by factors such as noise, scan duration, and scan interval. We propose that this component underlying the ongoing activity represents an individual-specific baseline pattern of brain activity.

Non-invasive determination of amyloid-β peptide (Aβ) and tau deposition are important for early diagnosis and therapeutic intervention for Alzheimer's disease (AD) and non-AD tauopathies. In the present study, we investigated the capacity of a novel radioiodinated compound AD-DRK (I-AD-DRK) with 50% inhibitory concentrations of 11 nM and 2 nM for Aβ and tau aggregates, respectively, as a single photon emission computed tomography (SPECT) ligand in living brains. In vitro and ex vivo autoradiography with I-AD-DRK was performed in postmortem human and two transgenic (Tg) mice lines with either fibrillar Aβ or tau accumulation, APP23 and rTg4510 mice. SPECT imaging of I-AD-DRK was performed in APP23 mice to investigate the ability of AD-DRK to visualize fibrillar protein deposition in the living brain. In-vitro autoradiogram of I-AD-DRK showed high specific radioactivity accumulation in the temporal cortex and hippocampus of AD patients and the motor cortex of progressive supranuclear palsy (PSP) patients enriched by Aβ and/or tau aggregates. Ex-vivo autoradiographic images also demonstrated a significant increase in I-AD-DRK binding in the forebrain of both APP23 and rTg450 mice compared to their corresponding non-Tg littermates. SPECT imaging successfully captured Aβ deposition in the living brain of aged APP23 mice. The present study developed a novel high-contrast SPECT agent for assisting the diagnosis of AD and non-AD tauopathies, likely benefiting from its affinity for both fibrillar Aβ and tau.

Neuroimaging studies have identified functional and structural brain circuits that support reading. However, much less is known about how reading-related functional dynamics are constrained by white matter structure. Network control theory proposes that cortical brain dynamics are linearly determined by the white matter connectome, using control energy to evaluate the difficulty of the transition from one cognitive state to another. Here we apply this approach to linking brain dynamics with reading ability and disability in school-age children. A total of 51 children ages 8.25 -14.6 years performed an in-scanner rhyming task in visual and auditory modalities, with orthographic (spelling) and phonological (rhyming) similarity manipulated across trials. White matter structure and fMRI activation were used conjointly to compute the control energy of the reading network in each condition relative to a null fixation state. We then tested differences of control energy across trial types, finding higher control energy during non-word reading than word reading, and during incongruent trials than congruent trials. ROI analyses further showed a dissociation between control energy of the left fusiform and superior temporal gyrus depending on stimulus modality, with higher control energy for visual modalities in fusiform and higher control energy for auditory modalities in STG. Together, this study highlights that control theory can explain variations on cognitive demands in higher-level abilities such as reading, beyond what can be inferred from either functional or structural MRI measures alone.

The electroencephalogram (EEG) exhibits characteristics of complexity and strong randomness. Existing deep learning models for EEG typically target specific objectives and datasets, with their scalability constrained by the size of the dataset, resulting in limited perceptual and generalization abilities. In order to obtain more intuitive, concise, and useful representations of brain activity, we constructed a reconstruction-based self-supervised learning model for EEG based on Variational Autoencoder (VAE) with separate frequency bands, termed variational auto-encoder for EEG (VAEEG). VAEEG achieved outstanding reconstruction performance. Furthermore, we validated the efficacy of the latent representations in three clinical tasks concerning pediatric brain development, epileptic seizure, and sleep stage classification. We discovered that certain latent features: 1) correlate with adolescent brain developmental changes; 2) exhibit significant distinctions in the distribution between epileptic seizures and background activity; 3) show significant variations across different sleep cycles. In corresponding downstream fitting or classification tasks, models constructed based on the representations extracted by VAEEG demonstrated superior performance. Our model can extract effective features from complex EEG signals, serving as an early feature extractor for downstream classification tasks. This reduces the amount of data required for downstream tasks, simplifies the complexity of downstream models, and streamlines the training process.

The neural basis of sex-related differences in processing negative emotions remains poorly understood. The amygdala-related fiber pathways serve as the neuroanatomical foundation for emotion processing. However, the precise sex-related variations within these pathways remain largely elusive. Using diffusion magnetic resonance imaging data from 418 healthy individuals, we identified sex differences in white-matter microstructures of the striato-amygdaloid-prefrontal tracts, particularly the amygdala (Amy)-medial prefrontal cortex (mPFC) pathway. These differences were associated with various neurobiological factors, including pain-related negative emotions, pain sensitivity, neurotransmitter receptors, and gene expressions in the human brain. Our findings suggested that the Amy-mPFC pathway may serve as a neuroanatomical foundation for sex-specific negative emotion processing, driven by specific genetic and neurotransmitter profiles. Notably, we also found similar sex differences in this pathway in an infant imaging dataset, hinting at its developmental significance as a precursor to sex differences in adulthood. These findings underscore the importance of the striato-amygdaloid-prefrontal tracts in sex-related differences in processing negative emotions. This may enhance our understanding of sex-specific emotion regulation and potentially inform future research on strategies for preventing and diagnosing emotional regulation disorders across sexes.

Excessive iron accumulation in the brain has been implicated in Parkinson's disease (PD). However, the patterns and probable sequences of iron accumulation across the PD brain remain largely unknown. This study aimed to explore the sequence of iron accumulation across the PD brain using R* mapping and a relaxometry covariance network (RCN) approach.

In accelerated MRI, the robust artificial-neural-network for k-space interpolation (RAKI) method is an attractive learning-based reconstruction that does not require additional training data. This study was focused on obtaining high quality MR images from regular under-sampled multi-coil k-space data using a high-pass filtered RAKI (HP-RAKI) reconstruction without any extra training data. MRI scan from human subjects was under-sampled with a regular pattern using skipped phase encoding and a fully sampled k-space center. A high-pass (HP) filter was applied in k-space to reduce image support to facilitate linear prediction. The HP filtered k-space center was used to train the RAKI network without any extra training data. The unacquired k-space data can be predicted from a trained RAKI network with optimized parameters. Final reconstruction was obtained after performing an inverse HP filtering for the predicted k-space data. This HP-RAKI method can be extended to corresponding residual structure (HP-rRAKI). HP-RAKI was compared with GRAPPA, HP-GRAPPA, RAKI and MW-RAKI algorithms, and HP-rRAKI was compared with corresponding residual extensions, including rRAKI and MW-rRAKI, all qualitatively and quantitatively using visual inspection and such metrics as SSIM and PSNR. HP-RAKI and HP-rRAKI were found to be effective in reconstructing MR images even at high acceleration factors. HP-RAKI and HP-rRAKI compared favorably with other algorithms. Using high-pass filtered central k-space data for training, HP-RAKI offers higher reconstruction quality for regularly under-sampled multi-coil k-space data without any extra training data. It has shown promising capabilities for fast MRI applications, especially those lacking fully sampled training data.

The processing of kinematic information embedded in observed actions is an essential ability for understanding others' behavior. Previous research showed that the action observation network (AON) may encode some action kinematic features. However, our understanding of how direction and velocity are encoded within the AON is still limited. In this study, we employed event-related fMRI to investigate the neural substrates specifically activated during observation of hand grasping actions presented as point-light displays, performed with different directions (right, left) and velocities (fast, slow). Twenty-three healthy adult participants took part in the study. To identify brain regions differentially recruited by grasping direction and velocity, univariate and multivariate pattern analysis (MVPA) were performed. The results of univariate analysis demonstrate that direction is encoded in occipito-temporal and posterior visual areas, while velocity recruits lateral occipito-temporal, superior parietal and intraparietal areas. Results of MVPA further show: a) a significant decoding accuracy of both velocity and direction at the network level; b) the possibility to decode within lateral occipito-temporal and parietal areas both direction and velocity; c) a contribution of bilateral premotor areas to velocity decoding models. These results indicate that posterior parietal nodes of the AON are mainly involved in coding grasping direction and that premotor regions are crucial for coding grasping velocity, while lateral occipito-temporal cortices play a key role in encoding both parameters. The current findings could have implications for observational-based rehabilitation treatments of patients with motor disorders and artificial intelligence-based hand action recognition models.

Accumulation of pathological tau is one of the primary causes of Primary Progressive Aphasia (PPA). The glymphatic system is crucial for removing metabolite waste from the brain whereas impairments in glymphatic clearance in PPA are poorly understood. Thus, this study aims to investigate the role of dysregulated macroscopic cerebrospinal fluid (CSF) movement in PPA. Fifty-six PPA individuals and ninety-four healthy controls were included in our analysis after excluding those with excessive head motions during the scan. The coupling strength between blood-oxygen-level-dependent (BOLD) signals in the gray matter and CSF flow was calculated using Pearson correlation and compared between the groups. Its associations with clinical characteristics including scores from Clinical Dementia Rating (CDR), Mini-Mental State Exam, Geriatric Depression Scale and with morphological measures in the hippocampus and entorhinal cortex were examined. PPA subjects exhibited weaker global BOLD-CSF coupling compared to HCs, indicating impairments in glymphatic function in the patients (p = 0.01). In the PPA but not HC group, global BOLD-CSF coupling correlated with the CDR scores (p = 0.04) and hippocampal volume (p = 0.009). The observed decoupling between global brain activity and CSF flow and its association with symptomatology and brain structural changes in PPA converges with previous reports on the same measure in other neurodegenerative diseases. These findings support the potential role of global BOLD-CSF coupling as a noninvasive marker for glymphatic dysregulation in PPA.

Partnership investment is a common form of business where investors have different levels of power and need to persuade each other to reach a consensus. This study investigated the neural mechanisms underlying the impact of decision-making power on persuasive communication in partnership investment, aiming to provide neural evidence to test two competing hypotheses: the power-responsibility hypothesis and the power-overconfidence hypothesis. Using functional near-infrared spectroscopy (fNIRS), we recorded brain activity from persuader-receiver dyads as they engaged in a partnership investment task. Behavioral results showed that receivers' decisions were more affected by persuaders' persuasive messages when receivers had dominant decision-making power. Neurally, the functional connectivity (FC) between the left and right temporo-parietal junctions (lTPJ and rTPJ) of the receiver was significantly increased by their decision-making power. Additionally, we identified four pairs of interpersonal neural synchronization (INS) that exhibited significant enhancement when persuaders used numeric persuasion rather than non-numeric persuasion: lTPJ-rTPJ, left superior temporal gyrus (lSTG)-rTPJ, left middle temporal gyrus (lMTG)-rTPJ, and medial prefrontal cortex (mPFC)-lTPJ. The decision-making power amplified the INS difference in the last three pairs. Furthermore, using a support vector machine (SVM) algorithm, the INS could accurately predict receivers' adoption of persuasive messages when they held dominant decision-making power. Finally, we found that FC at lTPJ-rTPJ and INS at lSTG-rTPJ were positively associated with receivers' adoption of persuasive messages as well. Our study clarifies how decision-making power alters the way individuals process persuasive messages in partnership investment, providing insights into the neural basis of persuasion in group decision-making contexts and supporting the power-responsibility hypothesis.

Complex actions require more cognitive and motor control than simple ones. Literature shows that to face complexity, the brain must make a compromise between available resources usually giving priority to motor control. However, literature has minimally explored the effect of the motor response complexity on brain processing associated with cognitive tasks. Consequently, it is unknown whether carrying out a cognitive task requiring motor responses of increasing complexity could reduce cognitive processing keeping stable motor control. Therefore, this study aims to investigate possible modulations exerted by increasing motor response complexity in a cognitive task on brain processing. To this aim, we analyzed the event-related potentials and behavioral responses during a cognitive task with increasing complexity of the required motor response (keypress, reaching and stepping). Results showed the increasing motor complexity enhances early visual and attentional processing (P1 and N1 components) but reduces the late post-perceptual cognitive control (P3 component). Additionally, we found a component following the P3 which was specific for stimuli requiring a response. This component, labeled N750, increased amplitude along with the response motor complexity. Behaviorally, response accuracy was not affected by complexity. Results indicated that in cognitive tasks stimulus processing is affected by the complexity of the motor response. Complex responses require a greater investment of early perceptual and attentional resources, but at late phases of processing, cognitive resources are less available in favor of motor resources. This confirms the idea of the motor-priority cognitive-motor trade-off of the brain.

Bilingual individuals manage multiple languages that align in conceptual meaning but differ in forms and structures. While prior research has established foundational insights into the neural mechanisms in bilingual processing, the extent to which the first (L1) and second language (L2) systems overlap or diverge across different linguistic components remains unclear. This study probed the neural underpinnings of syntactic and semantic processing for L1 and L2 in Chinese-English bilinguals (N=44) who performed sentence comprehension tasks and an N-back working memory task during functional MRI scanning. We observed that the increased activation for L2 processing was within the verbal working memory network, suggesting a greater cognitive demand for processing L2. Crucially, we looked for brain regions showing adaptation to the repetition of semantic information and syntactic structure and found more robust adaptation effects in L1 in the middle and superior temporal cortical areas. The differential adaptation effects between L1 and L2 were more pronounced for the semantic condition. Multivariate pattern analysis further revealed distinct neural sensitivities to syntactic and semantic representations between L1 and L2 across frontotemporal language regions. Our findings suggest that while L1 and L2 engage similar neural systems, finer representation analyses uncover distinct neural patterns for both semantic and syntactic aspects in the two languages. Further research involving language pairs of varying linguistic distances is essential to deepen our understanding of these effects.

Previous studies have shown that the visual word form area (VWFA) has structural and intrinsic functional connectivity with both language and attention networks. Nevertheless, it is still unclear how the functional connectivity pattern of the VWFA is regulated by processing demands induced by experimental tasks, and whether processing demands differentially regulate the posterior (VWFA-1) and anterior (VWFA-2) subregions of the VWFA. To address these questions, the present study adopted two tasks varying in processing demands (i.e., verbal and non-verbal tasks), and used generalized psychophysiological interaction (gPPI) and dynamic causal modeling (DCM) analyses to explore the task-dependent functional connectivity patterns of the two subregions of the VWFA. Activation analysis revealed that the VWFA-2 showed higher activation for the verbal task than the non-verbal task, while there were no activation differences in the VWFA-1 after controlling for the stimulus driven effects. Functional and effective connectivity analyses revealed that, for both VWFA-1 and VWFA-2, the verbal task enhanced connections from VWFAs to the ventral language regions (e.g., the left orbital frontal cortex), while the non-verbal task enhanced connections from VWFAs to the dorsal visuospatial regions (e.g., the left intraparietal sulcus). Results of the present study indicate that processing demands induced by tasks modulate both the local activity and functional connectivity patterns of the VWFA, providing new insights for understanding its domain-general function.

The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.