Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

Multifractal dynamic changes of spontaneous brain activity in psychiatric disorders: Adult attention deficit-hyperactivity disorder, bipolar disorder, and schizophrenia

It is one of the strategies to study the complexity of spontaneous fluctuation of brain neurons based on resting-state functional magnetic resonance imaging (rs-fMRI), but the multifractal characteristics of spontaneous fluctuation of brain neurons in psychiatric diseases need to be studied. Therefore, this paper will study the multifractal spontaneous brain activity changes in psychiatric disorders using the multifractal detrended fluctuation analysis algorithm based on the UCLA datasets. Specifically: (1) multifractal characteristics in adult attention deficit-hyperactivity disorder (ADHD), bipolar disorder (BP), and schizophrenia (SCHZ); (2) the source of those multifractal characteristics. Results showed that for adult ADHD, BP, and SCHZ, all 6 functional brain regions exhibit multifractal characteristics, and the multifractal spectrum shows a reduction in bell-shaped asymmetry, unlike the intensity of healthy control (HC) asymmetry. Besides, compared with HC, the multifractal sources of all functional brain regions were fat-tail probability distribution and the long-range dependence correlation, but the intensity of fat-tail probability distribution was decreased and the long-range dependence correlation was increased. The results provide a reference for further understanding the complexity of spontaneous fluctuation of neurons in psychiatric disorders.

Surrogate data analyses of the energy landscape analysis of resting-state brain activity

The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences.

Rehabilitation training robot using mirror therapy for the upper and lower limb after stroke: a prospective cohort study

BACKGROUND

This prospective cohort study was designed to investigate and compare the effectiveness of rehabilitation training robots versus conventional rehabilitation training on stroke survivors by monitoring alterations in brain network of stroke patients before and after robot intervention.

METHODS

Between September 2020 and November 2021, stroke patients at four grade-A tertiary hospitals underwent limb rehabilitation training. Of the total of participants, 117 patients received conventional limb rehabilitation, 93 patients participated in upper-limb robot training, and 103 patients underwent lower-limb robot training. The measured outcomes included modified Barthel Index (MBI), Fugl-Meyer assessment subscale (FMA), and manual muscle testing (MMT). Functional magnetic resonance imaging (fMRI) was conducted on 30 patients to assess changes in the brain network. Data were mainly analyzed based on the Intention-to-Treat (ITT) principle.

RESULTS

Post-interventional analysis utilizing linear mixed models in ITT analysis revealed that the robot training group had greater enhancements compared to the conventional limb rehabilitation training group. Notably, the shoulder flexor strength (P = 0.043) was significantly higher in the upper-limb group. On the other hand, hip flexor strength (P < 0.001), hip extensor strength (P < 0.001), knee extensor strength (P = 0.013), ankle dorsiflexion strength (P < 0.001) and ankle plantarflexor strength (P < 0.001) were significantly higher in the lower-limb group. In the upper-limb group, region-of-interest (ROI) -to-ROI analysis revealed enhanced functional connectivity between the left hemisphere's motor control region and the auditory network. ROI-to-ROI analysis primarily showed enhanced interhemispheric functional connectivity in the lower-limb group, specifically between right the hemisphere's motor control region (central opercular cortex) and left hemisphere's primary motor area in the precentral gyrus.

CONCLUSIONS

According to our research findings, upper- and lower-limb rehabilitation robots demonstrated great potential in promoting motor function recovery in stroke patients. Robot-assisted training offers an alternative treatment method with comparable efficacy to traditional rehabilitation. Large-scale randomized controlled trials are needed to confirm these results.

TRIAL REGISTRATION

The study was registered on the Chinese Clinical Trial Registry (ChiCTR1800019783).

Topography of putative bi-directional interaction between hippocampal sharp-wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Analyzing the brain's dynamic response to targeted stimulation using generative modeling

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Functional brain connectivity in early adolescence after hypothermia-treated neonatal hypoxic-ischemic encephalopathy

BACKGROUND

Neonatal hypoxic-ischemic encephalopathy (HIE) injures the infant brain during the basic formation of the developing functional connectome. This study aimed to investigate long-term changes in the functional connectivity (FC) networks of the adolescent brain following neonatal HIE treated with therapeutic hypothermia (TH).

METHODS

This prospective, population-based cohort study included all infants (n = 66) with TH-treated neonatal HIE in Stockholm during 2007-2009 and a control group (n = 43) of children with normal neonatal course. Assessment with resting-state functional magnetic resonance imaging (fMRI) was performed at Karolinska Institutet, Stockholm at age 9-12 years.

RESULTS

fMRI data met quality criteria for 35 children in the HIE-cohort (mean [SD] age at MRI: 11.2 [0.74] years, 46% male) and 30 children in the control group (mean [SD] age at MRI: 10.1 [0.78] years, 53% male). Adverse outcome was present in 40% of children in the HIE-cohort. Non-parametric statistical analysis failed to detect any significant (p < 0.001) alterations of FC networks in the HIE-cohort, nor between children in the HIE-cohort with or without neurological symptoms.

CONCLUSION

Findings of persistent alterations in specific functional networks did not remain significant after correction for multiple comparisons in this cohort of adolescent children exposed to TH-treated neonatal HIE.

IMPACT

Neonatal hypoxic-ischemic encephalopathy (HIE) could not be associated with alterations in functional connectivity in this cohort of adolescent children. Findings of aberrant connectivity identified in two functional networks were no longer significant after correction for multiple comparisons. Larger, multi-center studies are needed to understand whether network abnormalities persist long term and are related to outcomes in neonatal HIE.

Dark brain energy: Toward an integrative model of spontaneous slow oscillations

Neural oscillations facilitate the functioning of the human brain in spatial and temporal dimensions at various frequencies. These oscillations feature a universal frequency architecture that is governed by brain anatomy, ensuring frequency specificity remains invariant across different measurement techniques. Initial magnetic resonance imaging (MRI) methodology constrained functional MRI (fMRI) investigations to a singular frequency range, thereby neglecting the frequency characteristics inherent in blood oxygen level-dependent oscillations. With advancements in MRI technology, it has become feasible to decode intricate brain activities via multi-band frequency analysis (MBFA). During the past decade, the utilization of MBFA in fMRI studies has surged, unveiling frequency-dependent characteristics of spontaneous slow oscillations (SSOs) believed to base dark energy in the brain. There remains a dearth of conclusive insights and hypotheses pertaining to the properties and functionalities of SSOs in distinct bands. We surveyed the SSO MBFA studies during the past 15 years to delineate the attributes of SSOs and enlighten their correlated functions. We further proposed a model to elucidate the hierarchical organization of multi-band SSOs by integrating their function, aimed at bridging theoretical gaps and guiding future MBFA research endeavors.

Altered coupling relationships across resting-state functional connectivity measures in schizophrenia, bipolar disorder, and attention deficit/hyperactivity disorder

Resting-state functional connectivity (rsFC) measures have enjoyed significant success in discovering the neuropathological characteristics of schizophrenia (SZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). However, it is unknown whether and how the spatial and temporal coupling relationships across rsFC measures would be altered in these psychiatric disorders. Here, resting-state fMRI data were obtained from a transdiagnostic sample of healthy controls (HC) and individuals with SZ, BD, and ADHD. We used Kendall's W to compute volume-wise and voxel-wise concordance across rsFC measures, followed by group comparisons. In terms of the spatial coupling, both SZ and BD individuals exhibited decreased volume-wise concordance compared with HC. Regarding the temporal coupling, SZ individuals showed decreased voxel-wise concordance in the right lateral occipital cortex relative to HC. BD individuals exhibited decreased voxel-wise concordance in the bilateral basal forebrain and bilateral superior/middle temporal gyrus compared to HC. Additionally, correlation analyses demonstrated positive associations of voxel-wise concordance in the left basal forebrain with negative symptoms including alogia and affective flattening in pooled SZ and BD individuals. Our findings of distinct patterns of spatial and temporal decoupling across rsFC measures among SZ, BD, and ADHD may provide unique insights into the neuropathological mechanisms of these psychiatric disorders.

Fast blood flow index reconstruction of diffuse correlation spectroscopy using a back-propagation-free data-driven algorithm

This study introduces a fast and accurate online training method for blood flow index (BFI) and relative BFI (rBFI) reconstruction in diffuse correlation spectroscopy (DCS). We implement rigorous mathematical models to simulate the auto-correlation functions (g 2) for semi-infinite homogeneous and three-layer human brain models. We implemented a fast online training algorithm known as random vector functional link (RVFL) to reconstruct BFI from noisy g 2. We extensively evaluated RVFL regarding both speed and accuracy for training and inference. Moreover, we compared RVFL with extreme learning machine (ELM) architecture, a conventional convolutional neural network (CNN), and three fitting algorithms. Results from semi-infinite and three-layer models indicate that RVFL achieves higher accuracy than the other algorithms, as evidenced by comprehensive metrics. While RVFL offers comparable accuracy to CNNs, it boosts training speeds that are 3900-fold faster and inference speeds that are 19.8-fold faster, enhancing its generalizability across different experimental settings. We also used g 2 from one- and three-layer Monte Carlo (MC)-based in-silico simulations, as well as from analytical models, to compare the accuracy and consistency of the results obtained from RVFL and ELM. Furthermore, we discuss how RVFL is more suitable for embedded hardware due to its lower computational complexity than ELM and CNN for training and inference.

Spatial dynamics of spontaneous activity in the developing and adult cortices

Even in the absence of external stimuli, the brain remains remarkably active, with neurons continuously firing and communicating with each other. It is not merely random firing of individual neurons but rather orchestrated patterns of activity that propagate throughout the intricate network. Over two decades, advancements in neuroscience observation tools for hemodynamics, membrane potential, and neural calcium signals, have allowed researchers to analyze the dynamics of spontaneous activity across different spatial scales, from individual neurons to macroscale brain networks. One of the remarkable findings from these studies is that the spatial patterns of spontaneous activity in the developing brain are vastly different from those in the mature adult brain. Spatial patterns of spontaneous activity during development are essential for connection refinement between brain regions, whereas the functional role in the adult brain is still controversial. In this paper, I review the differences in spatial dynamics of spontaneous activity between developing and adult cortices. Then, I delve into the cellular mechanisms underlying spontaneous activity, especially its generation and propagation manner, to contribute to a deeper understanding of brain function and its development.

Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling

AIM

As a central component of schizophrenia psychopathology, negative symptoms result in detrimental effects on long-term functional prognosis. However, the neurobiological mechanism underlying negative symptoms remains poorly understood, which limits the development of novel treatment interventions. This study aimed to identify the specific neural fingerprints of negative symptoms in schizophrenia.

METHODS

Based on resting-state functional connectivity data obtained in a large sample (n = 132) of first-episode drug-naïve schizophrenia patients (DN-FES), connectome-based predictive modeling (CPM) with cross-validation was applied to identify functional networks that predict the severity of negative symptoms. The generalizability of identified networks was then validated in an independent sample of n = 40 DN-FES.

RESULTS

A connectivity pattern significantly driving the prediction of negative symptoms (ρ = 0.28, MSE = 81.04, P = 0.012) was identified within and between networks implicated in motivation (medial frontal, subcortical, sensorimotor), cognition (default mode, frontoparietal, medial frontal) and error processing (medial frontal and cerebellum). The identified networks also predicted negative symptoms in the independent validation sample (ρ = 0.37, P = 0.018). Importantly, the predictive model was symptom-specific and robust considering the potential effects of demographic characteristics and validation strategies.

CONCLUSIONS

Our study discovers and validates a comprehensive network model as the unique neural substrates of negative symptoms in schizophrenia, which provides a novel and comprehensive perspective to the development of target treatment strategies for negative symptoms.

Psychophysiological and psychological responses of touching plant behavior by tactile stimulation according to the foliage type

Urbanization-related stress has spurred interest in natural therapies, such as horticultural therapy, which leverages multisensory exposure to plants to enhance well-being through physical, psychological, and cognitive benefits. This study aimed to measure and compare the psychophysiological and psychological responses to tactile stimuli through plant contact based on the foliage type. Thirty adults (average age: 24.86 ± 2.68) participated in the study, and the foliage was categorized into four groups: soft (e.g., Stachys byzantina, Adiantum raddianum, and Asparagus plumosus var. nanus), smooth (e.g., Peperomia obtusifolia, Ficus benghalensis, and Epipremnum aureum), stiff (e.g., Chamaeshparis thyoides Red Star, Platycladus orientalis, and Cupressus macrocarpa), and rough (e.g., Rhapis excelsa, Nephrolepis cordifolia 'Duffii', and Ardisia pusilla 'Variegata') plant groups. The participants touched the plants for 90 s, and the concentration of oxyhemoglobin (oxy-Hb) in the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). Additionally, a semantic differential method (SDM) evaluation tool was used to assess the psychological responses of each treatment group. When comparing the four tactile treatment groups (soft, smooth, stiff, and rough), the oxy-Hb concentration in the PFC area was lowest during tactile stimulation of smooth plants and highest during soft plant stimulation. Sex-based comparison of oxy-Hb concentrations showed significant differences in the overall PFC area for all four tactile treatment groups in males (p < 0.001). Specifically, when touching soft plants, the oxy-Hb concentration in females was significantly lower than that in males (p < 0.001). According to the SDM, the tactile stimulation of soft and smooth plants elicited the most relaxation, comfort, and favorable responses (p < 0.001). When touching smooth plants, the oxy-Hb concentration of the participant was the lowest, and according to the SDM, they reported the most soothing response. Summarily, the participants in the smooth plant group exhibited a trend of decreased oxy-Hb concentrations and concurrently experienced a sense of psychological stability. We established those tactile stimuli based on foliage texture resulted in different psychophysiological and psychological responses depending on the plant treatment group and sex.

When neuromodulation met control theory

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

Nebulized seabuckthorn seed oil inhalation attenuates Alzheimer's disease progression in APP/PS1 mice

Seabuckthorn (Hippophae rhamnoides L.) is known for its medicinal properties in treating various diseases, including neurological conditions. However, the therapeutic effect of inhaled seabuckthorn seed oil (SSO) on Alzheimer's disease (AD) remains not fully understood. This study explores the effects of nebulized inhalation of SSO in 9-month-old APP/PS1 mice over 21 days. The results showed that nebulized SSO improved memory and cognition. Using 7.0T MRI to monitor blood oxygenation level dependent (BOLD) signals revealed that SSO altered the Amplitude of Low Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) signaling such as in the amygdala and substantia innominate, and hippocampus. Enzyme-linked immuno sorbent assay (ELISA) and pathological analyses indicated reduced neuroinflammation in plasma and brain, decreased neuronal necrosis, lower β-amyloid (Aβ) protein levels, reduced amyloid deposition, and increased tyrosine hydroxylase activity. Additionally, SSO promoted gut microbiota remodeling by increasing alpha diversity and boosting levels of probiotics such as Verrucomicrobia, Bifidobacterium, Prevotella, and Akkermansia, without adverse effects on lung tissue. Nebulized inhalation of SSO may slow AD progression by modulating inflammation and amyloid deposition. Nebulized inhalation offered a potential method for enhancing drug delivery across the blood-brain barrier with reduced systemic side effects.

Disrupted intersubject variability architecture in structural and functional brain connectomes in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous condition characterized by significant intersubject variability in clinical presentations. Recent neuroimaging studies have indicated that MDD involves altered brain connectivity across widespread regions. However, the variability in abnormal connectivity among MDD patients remains understudied.

METHODS

Utilizing a large, multi-site dataset comprising 1,276 patients with MDD and 1,104 matched healthy controls, this study aimed to investigate the intersubject variability of structural covariance (IVSC) and functional connectivity (IVFC) in MDD.

RESULTS

Patients with MDD demonstrated higher IVSC in the precuneus and lingual gyrus, but lower IVSC in the medial frontal gyrus, calcarine, cuneus, and cerebellum anterior lobe. Conversely, they exhibited an overall increase in IVFC across almost the entire brain, including the middle frontal gyrus, anterior cingulate cortex, hippocampus, insula, striatum, and precuneus. Correlation and mediation analyses revealed that abnormal IVSC was positively correlated with gray matter atrophy and mediated the relationship between abnormal IVFC and gray matter atrophy. As the disease progressed, IVFC increased in the left striatum, insula, right lingual gyrus, posterior cingulate, and left calcarine. Pharmacotherapy significantly reduced IVFC in the right insula, superior temporal gyrus, and inferior parietal lobule. Furthermore, we found significant but distinct correlations between abnormal IVSC and IVFC and the distribution of neurotransmitter receptors, suggesting potential molecular underpinnings. Further analysis confirmed that abnormal patterns of IVSC and IVFC were reproducible and MDD specificity.

CONCLUSIONS

These results elucidate the heterogeneity of abnormal connectivity in MDD, underscoring the importance of addressing this heterogeneity in future research.

Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. However, to date, the empirical cognitive characterization of these networks has been limited. The present study introduces the Groupe d'Imagerie Neurofonctionnelle Network Atlas, a comprehensive brain atlas featuring 33 resting-state networks. Based on the resting-state data of 1812 participants, the atlas was developed by classifying independent components extracted individually, ensuring consistent between-subject detection. We further explored the cognitive relevance of each GINNA network using Neurosynth-based meta-analytic decoding and generative null hypothesis testing. Significant cognitive terms for each network were then synthesized into appropriate cognitive processes through the consensus of six authors. The GINNA atlas showcases a diverse range of topological profiles, reflecting a broad spectrum of the known human cognitive repertoire. The processes associated with each network are named according to the standard Cognitive Atlas ontology, thus providing opportunities for empirical validation.

White matter microstructural integrity mediates associations between prenatal endocrine-disrupting chemicals exposure and intelligence in adolescents

Per- and polyfluoroalkyl substances (PFAS) and phthalic acid esters (PAEs) are well-known endocrine-disrupting chemicals (EDCs) that potentially affect child neurodevelopment. We aimed to investigate the effects of prenatal exposure to PFAS and PAEs on macro- and micro-structural brain development and intelligence in adolescents using multimodal neuroimaging techniques. We employed structural magnetic resonance imaging (MRI) and various diffusion MRI techniques, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), to assess the gray-matter macrostructure and white-matter microstructural integrity and complexity. Participants were drawn from a birth cohort of 52 mother-child pairs in central Taiwan recruited in 2001, and the adolescent intelligence quotient (IQ) scores were assessed using the Wechsler Intelligence Scale. Nine PFAS concentrations of cord blood and maternal serum samples were obtained from the children's mothers during the third trimester of pregnancy (27-40 weeks) using a liquid chromatography system coupled to a triple-quadrupole mass spectrometer, while maternal urinary phthalates were used to evaluate PAEs exposure. Our results showed significant associations between prenatal exposure to PFAS and phthalates with changes in specific fronto-parietal regions of the adolescent male brain, including reduced cortical thickness in the inferior frontal gyrus and right superior parietal cortex, which are involved in language, memory, and executive function. A dose-response association was observed, with higher levels of PFAS and PAE exposure modulating altered white-matter fiber integrity in the superior cerebellar peduncle and inferior cerebellar peduncle of the male and female adolescent brains. In addition, higher levels of prenatal exposure to EDCs were associated with lower IQ scores in adolescents. Mediation analyses further revealed that white-matter microstructure of inter-hemispheric and cerebellar fibers mediated the association between prenatal EDC exposure and adolescent IQ scores in female adolescents. Our multimodal human neuroimaging findings suggest that prenatal exposure to EDCs may have long-lasting effects on neuroanatomical development, neural fiber connectivity, and intelligence in adolescents, and highlight the importance of using advanced diffusion imaging techniques, including DKI and NODDI, to detect neurodevelopmental changes and their brain-behavioral consequences with the risks associated with these environmental exposures.

Mapping brain neural networks in stress brain connectivity

Stress can cause severe damage to the CNS and contribute to an increased risk of neurological and psychiatric disorders. Gaining more insight into the neurobiology of stress is essential to treating neurological disorders associated with stress, which account for a high percentage of the world's disease burden. However, because of complicated variations in stressor types, stress perception, and preceding exposure to stressors, studying the impacts of stress is challenging. Gender, age, and timing are other crucial variables that can influence the stress response. Behavioral, physiological, genetic, and cellular/molecular neuroscience methodologies have all been widely applied in various research contexts to examine the neurobiological impacts of stress. Furthermore, because these approaches are invasive and hence undesirable or impractical for use in humans, they are frequently challenging to adapt to a therapeutic context. As an alternative to invasive procedures, functional neuroimaging approaches are starting to be developed. We discuss in this chapter brain neural networks under stress brain connection.

Frequency-dependent changes in the amplitude of low-frequency fluctuations in post stroke apathy: a resting-state fMRI study

Background

Apathy is a prevalent psychiatric condition after stroke, affecting approximately 30% of stroke survivors. It is associated with slower recovery and an increased risk of depression. Understanding the pathophysiological mechanisms of post stroke apathy (PSA) is crucial for developing targeted rehabilitation strategies.

Methods

In this study, we recruited a total of 18 PSA patients, 18 post-stroke non-apathy (NPSA) patients, and 18 healthy controls (HCs). Apathy was measured using the Apathy Evaluation Scale (AES). Resting-state functional magnetic resonance imaging (rs-fMRI) was utilized to investigate spontaneous brain activity. We estimated the amplitude of low-frequency fluctuation (ALFF) across three different frequency bands (typical band: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) and the fractional amplitude of low-frequency fluctuation (fALFF).

Results

Band-specific ALFF differences among the three groups were analyzed. Significant differences were found in the typical band within the left lingual gyrus, right fusiform gyrus, right superior temporal gyrus (STG), and left insula. In the slow-4 band, significant differences were observed in the left middle frontal gyrus (MFG) and right STG. In the slow-5 band, significant differences were identified in the left calcarine cortex and right insula. For fALFF values, significant differences were found in the left lingual gyrus and right thalamus. Moreover, positive correlations were observed between AES scores and the ALFF values in the right STG (r = 0.490, p = 0.002) in the typical band, left MFG (r = 0.478, p = 0.003) and right STG (r = 0.451, p = 0.006) in the slow-4 band, and fALFF values of the right thalamus (r = 0.614, p < 0.001).

Conclusion

This study is the first to investigate the neural correlates of PSA using voxel-level analysis and different ALFF banding methods. Our findings indicate that PSA involves cortical and subcortical areas, including the left MFG, right STG, and right thalamus. These results may help elucidate the neural mechanisms underlying PSA and could serve as potential neuroimaging indicators for early diagnosis and intervention.

A Narrative Review on Cognitive Impairment in Type 2 Diabetes: Global Trends and Diagnostic Approaches

Diabetes is a chronic disease that affects many people, with both its incidence and prevalence rising globally. Diabetes can lead to various complications, among which cognitive impairment in diabetic patients significantly impacts their daily life and blood glucose management, complicating treatment and worsening prognosis. Therefore, the early diagnosis and treatment of cognitive impairment are essential to ensure the health of diabetic patients. However, there is currently no widely accepted and effective method for the early diagnosis of diabetes-related cognitive impairment. This review aims to summarize potential screening and diagnostic methods, as well as biomarkers, for cognitive impairment in diabetes, including retinal structure and function examination, brain imaging, and peripheral blood biomarkers, providing valuable information and support for clinical decision making and future research.

Similarities and Differences in Resting-State Brain Activity Changes of Distinct Chronic Pain Types

OBJECTIVES

To explore neural similarities and differences between visceral and somatic pain by comparing spontaneous brain activity in patients with chronic temporomandibular disorder (TMD) and irritable bowel syndrome (IBS).

METHODS

Twenty eight IBS patients, 21 TMD patients, and 28 healthy controls (HC) underwent resting-state fMRI and behavioral assessments. The correlations between fMRI metrics such as the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC), and clinical manifestations were further analyzed.

RESULTS

Compared with HC, both patient groups demonstrated increased ALFF in right parahippocampal gyrus (PHG), insula, medial superior frontal gyrus (SFGmed), precentral gyrus (PreCG), and increased ReHo in right SFGmed and left supplementary motor area (SMA). Compared with IBS patients, TMD patients exhibited reduced ALFF in right SFGmed and insula, increased ALFF in right PHG and PreCG, decreased ReHo in right SFGmed and left lingual gyrus, and increased ReHo in left SMA. Both patient groups exhibited enhanced right PHG-related FC in left precuneus and right cingulate gyrus, and right insula-related FC in left superior temporal gyrus and right paracentral lobule. Specifically, IBS patients showed higher FC between right PHG and orbitofrontal cortex than TMD patients, which was negatively correlated with mood and gastrointestinal symptoms. Mediation analysis revealed that pain in TMD and gastrointestinal symptoms in IBS mediated these relationships.

CONCLUSION

Visceral and somatic pain share abnormal activity in multiple brain networks. Abnormalities in affective region present potential neuroimaging markers for pain disorders, with depression in somatic pain linked to pain intensity and in visceral pain to gastrointestinal symptoms.

Intrinsic brain functional connectivity mediates the relationship between psychological resilience and cognitive decline in ageing

Ageing individuals often experience cognitive decline and intrinsic functional connectivity (FC) changes. Psychological resilience, a personality trait that reflects the capacity to adapt and cope with age-related challenges, plays a key role in mitigating cognitive decline. In this study involving 101 older adults, we investigated how psychological resilience influences cognitive decline measured by processing speed. Particularly, we obtained resting-state functional magnetic resonance imaging (fMRI) to assess how intrinsic FC, represented by degree centrality, modulates the relationship between resilience and processing speed. Our results indicated while psychological resilience positively predicted processing speed, this relationship was mainly driven by education. Additionally, the degree centrality of both thalamus and caudate negatively correlated with processing speed and resilience. Notably, the degree centrality of both thalamus and caudate significantly mediated the relationship between resilience and processing speed. These findings suggest that psychological resilience could protect against age-related cognitive decline via its influence on FC in the thalamus and caudate, highlighting these areas as potential intervention targets for reducing cognitive decline in ageing people.

Simultaneous tACS-fMRI reveals state- and frequency-specific modulation of hippocampal-cortical functional connectivity

Non-invasive indirect hippocampal-targeted stimulation is of broad scientific and clinical interest. Transcranial alternating current stimulation (tACS) is appealing because it allows oscillatory stimulation to study hippocampal theta (3-8 Hz) activity. We found that tACS administered during functional magnetic resonance imaging yielded a frequency-, mental state- and topologically-specific effect of theta stimulation (but not other frequencies) enhancing right (but not left) hippocampal-cortical connectivity during resting blocks but not during task blocks. Control analyses showed that this effect was not due to possible stimulation-induced changes in signal quality or head movement. Our findings are promising for targeted network modulations of deep brain structures for research and clinical intervention.

Dynamic amplitude of low-frequency fluctuation links dark personalities to malevolent creative behavior

Malevolent creativity refers to the ability to generate ideas that cause harm to oneself or others. While previous research has touched on how personality traits influence malevolent creative behavior, the neural mechanisms involved remain underexplored. This study investigated the brain patterns associated with malevolent creative behavior and how these patterns are mediated by dark personality traits (Machiavellianism, narcissism, and psychopathy) and positive traits (internalization, symbolization, and honesty-humility). Our findings revealed that Machiavellianism mediated the relationship between the amplitude of low-frequency fluctuation (ALFF) in the left medial superior frontal gyrus (mSFG), pallidum (PAL), and middle temporal gyrus (MTG) and malevolent creative behavior, particularly in actions like hurting people or playing tricks. Psychopathy similarly mediated the link between the ALFF in the right orbital middle frontal gyrus (oMFG), right mSFG, left PAL, and left MTG and malevolent creative behavior. Additionally, Machiavellianism negatively mediated the relationship between the fractional ALFF (fALFF) of the left parahippocampal gyrus (PHG) and hurting people, as well as between the fALFF of the left inferior occipital gyrus (IOG) and playing tricks. The ALFF in the left mSFG and left MTG predicted playing tricks but also negatively predicted internalization and honesty-humility, which in turn reduced engagement in playing tricks. Finally, the fALFF of the left IOG negatively predicted playing tricks and positively predicted internalization, which again decreased playing tricks. These findings highlight the complex interaction between brain activity, personality traits, and malevolent creative behavior, offering a potential path for targeted interventions and further research into this interesting phenomenon.

Cerebello-Cerebral Resting-State Functional Connectivity in Poststroke Aphasia

Introduction: The influence of the cerebellum in poststroke aphasia recovery is poorly understood. Despite the right cerebellum being identified as a critical region involved in both language and cognitive functions, little is known about functional connections between the cerebellum and bilateral cortical hemispheres following stroke. This study investigated the relationship between chronic poststroke naming deficits and cerebello-cerebral resting-state functional connectivity (FC). Methods: Twenty-five cognitively normal participants and 42 participants with chronic poststroke aphasia underwent resting-state functional magnetic resonance imaging. Participants with aphasia also underwent language assessment. We conducted regions of interest (ROI)-to-ROI analyses to investigate the FC between the right cerebellar Crus I/II (seed ROI; Cereb1r/Cereb2r) and bilateral cortical language regions and compared these results to cognitively normal controls. Single-subject connectivity parameters were extracted and used as independent variables in a stepwise multiple linear regression model associating Boston Naming Test (BNT) score with FC measures. Results: FC analyses demonstrated correlations between the right cerebellar Crus I/II and both left and right cortical regions for both cognitively normal controls and stroke participants. Additionally, aphasia severity and lesion load had an effect on the cerebello-cerebral network connectivity in participants with aphasia. In a stepwise multiple linear regression, controlling for aphasia severity, time poststroke and lesion load, FC between the right Cereb2-left Cereb1 (standardized beta [std B]= -0.255, p < 0.004), right Cereb2-right anterior MTG (std B = 0.259, p < 0.004), and the right Cereb2-left anterior STG (std B = -0.208, p < 0.018) were significant predictors of BNT score. The overall model fit was R2 = 0.786 (p = 0.001). Conclusion: Functional connections between the right cerebellum and residual bilateral cerebral hemisphere regions may play a role in predicting naming ability in poststroke aphasia.

Altered brain functional connectivity in patients with tension-type headache

OBJECTIVE

To evaluate whether patients with tension-type headache (TTH) exhibit abnormal brain functional connectivity compared to healthy controls.

BACKGROUND

TTH is one of the most prevalent headache disorders throughout the world. The present study delves into brain functional connectivity in patients with TTH to enhance the understanding of its underlying pathophysiology.

METHODS

A cross-sectional study was conducted, enrolling patients with TTH diagnosed in line with the International Classification of Headache Disorders, 3rd edition beta criteria and a cohort of healthy controls (HCs). We used four metrics-global brain functional connectivity, functional connectivity, Granger causality analysis, and dynamic functional connectivity-to evaluate alterations of functional connectivity patterns in patients with TTH from both static and dynamic perspectives. Furthermore, correlational analyses were performed to explore the relationships between abnormal brain activities and clinical characteristics.

RESULTS

A total of 33 patients with TTH (mean age = 42.3; 13 males/20 females) and 30 HCs (mean age = 37.1; 13 males/17 females) were included in the current study. Compared to HCs, patients with TTH showed altered global brain functional connectivity in the right dorsolateral superior frontal gyrus (SFGdor, t = 4.60). Abnormal functional connectivity was also detected between the right SFGdor and the right superior temporal gyrus (t = 4.56). Furthermore, the right SFGdor exhibited altered information flow with several brain regions, including the left precuneus (t = 5.16), right middle temporal gyrus (MTG, t = 4.72/-4.41), right inferior temporal gyrus (t = 4.64), right caudate nucleus (t = 4.09), and right thalamus (THA, t = -4.04). In terms of dynamic functional connectivity, disconnection was observed between the right SFGdor and the right MTG (t = -3.10), right Rolandic operculum (ROL, t = 3.60), left opercular inferior frontal gyrus (t = -3.48), and left medial superior frontal gyrus (t = -3.00). In addition, the correlation analyses revealed that activities in the MTG (r = 0.48), THA (r = -0.38), and ROL (r = 0.36) were significantly correlated with disease duration, while THA activity was associated with Visual Analogue Scale scores (r = 0.50).

CONCLUSIONS

This study revealed alterations in both static and dynamic brain functional connectivity in patients with TTH within regions implicated in sensory perception, emotional processing, cognition, and pain regulation. These results may promote the understanding of the neural networks involved in TTH and potentially inform future therapeutic approaches for the condition.

QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab , an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4-6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics.

Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles

BACKGROUND

Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.

METHODS

We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.

RESULTS

We observed increased regional SC-FC coupling in the default mode network (t337 = 3.233) and decreased coupling in the frontoparietal network (t337 = -3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve = 0.853), support vector machine (area under the receiver operating characteristic curve = 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.

CONCLUSIONS

This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.

Abnormal dynamic functional networks during pain-free periods: Resting-state co-activation pattern analysis in primary dysmenorrhea: Abnormal dynamic functional networks in primary dysmenorrhea

Chronic pain alters the configuration of brain functional networks. Primary dysmenorrhea (PDM) is a form of chronic visceral pain, which has been identified spatial alterations in brain functional networks using static functional connectivity analysis methods. However, the dynamics alterations of brain functional networks during pain-free periovulation phase remain unclear. Using the co-activation pattern (CAP) method, we investigated the dynamic network characteristics of brain functional networks and their relationship with pain-related emotions in a sample of 59 women with PDM and 57 demographically matched healthy controls (HCs) during the pain-free periovulation phase. We observed that patients with PDM showed significant alterations in brain dynamics compared to HCs in the slow-4 (0.027-0.073 Hz) frequency band during the pain-free periovulation phase. Additionally, the fraction of time for CAP state 2 was positively correlated with the Pain Catastrophizing Scale-helplessness score, while the persistence time for CAP state 1 was positively correlated with the McGill Pain Questionnaire score. Our results provide new insights, suggesting that the atypical brain functional network dynamics may serve as a potential biological marker of patients with PDM during the pain-free periovulation phase.

Linking structural and functional changes during healthy aging and semantic dementia using multilayer brain network analysis

Healthy aging is characterized by frontal and diffuse brain changes, while certain age-related pathologies such as semantic dementia will be associated with more focal brain lesions, particularly in the temporo-parietal regions. These changes in structural integrity could influence functional brain networks. Here we use multilayer brain network analysis on structural (DWI) and functional (fMRI) data in younger and older healthy individuals and patients with semantic dementia. Relative to younger adults, results revealed lower levels of similarity of connectivity patterns between brain structure and function, and an increased network clustering in frontal regions in healthy older individuals. These changes were either associated with a preservation (similarity) and a decrease (clustering) in cognitive performance. Patients with semantic dementia showed an increase in the similarity of structural and functional connectivity patterns, as well as an increase in clustering in temporo-parietal regions. These changes were respectively associated with a preservation and a decrease in cognitive performance. These results provide a better characterization of distinct profiles of age- and pathology-brain network changes and their association with the preservation or the decline of cognitive functions.

Time-Varying Spatial Propagation of Brain Networks in fMRI Data

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level-dependent (BOLD) signal for 6-8 s, whereas the visual network propagates much quicker. We also show that summaries of network-specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Multi-domain Online Therapeutic Investigation Of Neurocognition (MOTION) - A randomized comparative-effectiveness study of two remotely delivered mind-body interventions for older adults with cognitive decline

BACKGROUND

Research suggest that mind-body movement programs have beneficial effects on cognitive outcomes for older adults with cognitive decline. However, few studies have directly compared specific approaches to mind-body movement or studied the impact of remote program delivery.

METHODS

In a 3-arm randomized controlled trial (RCT) for older adults with cognitive impairment, we are comparing a multidomain mind-body program that emphasizes movement, body awareness, personal meaningfulness, and social connection, and a traditional Chinese mind-body exercise (Tai Chi) to a health and wellness education control condition. All 3 interventions are delivered remotely two times per week (onehour per session) for 12 weeks. The two active interventions are live-streamed. Outcomes are assessed prior to, after, and 6-months after the interventions. The co-primary outcomes are changes on the Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-cog) and brain functional connectivity in the Default Mode Network (DMN). Secondary outcomes include measures of specific cognitive domains (e.g., executive function, attention), mobility, and self-report measures of general well-being, quality of life, social engagement, self- and attention-regulation.

CONCLUSION

This RCT will directly compare the effects of two mind-body movement programs versus an education control delivered remotely over 12 weeks on cognitive, neuroimaging, and participant-reported outcomes. If successful, these programs may provide scalable strategies for slowing cognitive decline, which could potentially delay dementia onset in some individuals.

TRIAL REGISTRATION ID

NCT05217849.

Reduced frontotemporal connectivity during a verbal fluency task in patients with anxiety, sleep, and major depressive disorders

Background

It has been well established that psychiatric disorders are often accompanied by cognitive dysfunction. Previous studies have investigated the verbal fluency task (VFT) for detecting executive function impairment in different psychiatric disorders, but the sensitivity and specificity of this task in different psychiatric disorders have not been explored. Furthermore, clarifying the mechanisms underlying variations in executive function impairments across multiple psychiatric disorders will enhance our comprehension of brain activity alternations among these disorders. Therefore, this study combined the VFT and the functional near-infrared spectroscopy (fNIRS) to investigate the neural mechanisms underlying the impairment of executive function across psychiatric disorders including anxiety disorder (AD), sleep disorder (SD) and major depressive disorder (MDD).

Methods

Two hundred and eight participants were enrolled including 52 AD, 52 SD, 52 MDD and 52 healthy controls (HCs). All participants completed the VFT while being monitored using fNIRS to measure changes in brain oxygenated hemoglobin (Oxy-Hb).

Results

Our results demonstrated that MDD, AD and SD exhibited decreased overall connectivity strength, as well as reduced connected networks involving the frontal and temporal regions during the VFT comparing to HC. Furthermore, the MDD group showed a reduction in connected networks, specifically in the left superior temporal gyrus and precentral gyrus, compared to the AD group.

Conclusion

Our study offers neural evidence that the VFT combined with fNIRS could effectively detect executive function impairment in different psychiatric disorders.

Ultra high density imaging arrays in diffuse optical tomography for human brain mapping improve image quality and decoding performance

Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid. Herein, simulations indicated reducing inter-optode spacing to 6.5 mm, creating a higher-density grid with more source-detector distances, would further improve image quality and noise-resolution tradeoff, with diminishing returns below 6.5 mm. We then constructed an ultra-high-density DOT system (6.5-mm spacing) with 140 dB dynamic range that imaged stimulus-evoked activations with 30-50% higher spatial resolution and repeatable multi-focal activity with excellent agreement with participant-matched fMRI. Further, this system decoded visual stimulus position with 19-35% lower error than previous HD-DOT, throughout occipital cortex.

QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab , an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4-6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics.

Investigating the intrinsic top-down dynamics of deep generative models

Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms. In this work, we study the generative dynamics of a recent extension of the DBN, the iterative DBN (iDBN), which more faithfully simulates neurocognitive development by jointly tuning the connection weights across all layers of the hierarchy. We characterize the number of states visited during top-down sampling and investigate whether the heterogeneity of visited attractors could be increased by initiating the generation process from biased hidden states. To this end, we train iDBN models on well-known datasets containing handwritten digits and pictures of human faces, and show that the ability to generate diverse data prototypes can be enhanced by initializing top-down sampling from "chimera states", which represent high-level features combining multiple abstract representations of the sensory data. Although the models are not always able to transition between all potential target states within a single-generation trajectory, the iDBN shows richer top-down dynamics in comparison to a shallow generative model (a single-layer Restricted Bolzamann Machine). We further show that the generated samples can be used to support continual learning through generative replay mechanisms. Our findings suggest that the top-down dynamics of hierarchical generative models is significantly influenced by the shape of the energy function, which depends both on the depth of the processing architecture and on the statistical structure of the sensory data.

fMRI Insights into Visual Cortex Dysfunction as a Biomarker for Migraine with Aura

Migraine with aura (MwA) is a common and severely disabling neurological disorder, characterised by transient yet recurrent visual disturbances, including scintillating scotomas, flickering photopsias, and complex geometric patterns. These episodic visual phenomena significantly compromise daily functioning, productivity, and overall quality of life. Despite extensive research, the underlying pathophysiological mechanisms remain only partially understood. Cortical spreading depression (CSD), a propagating wave of neuronal and glial depolarisation, has been identified as a central process in MwA. This phenomenon is triggered by ion channel dysfunction, leading to elevated intracellular calcium levels and excessive glutamate release, which contribute to widespread cortical hyperexcitability. Genetic studies, particularly involving the CACNA gene family, further implicate dysregulation of calcium channels in the pathogenesis of MwA. Recent advances in neuroimaging, particularly functional magnetic resonance imaging (fMRI), have provided critical insights into the neurophysiology of MwA. These results support the central role of CSD as a basic mechanism behind MwA and imply that cortical dysfunction endures beyond brief episodes, possibly due to chronic neuronal dysregulation or hyperexcitability. The visual cortex of MwA patients exhibits activation patterns in comparison to other neuroimaging studies, supporting the possibility that it is a disease-specific biomarker. Its distinctive sensory and cognitive characteristics are influenced by a complex interplay of cortical, vascular, and genetic factors, demonstrating the multifactorial nature of MwA. We now know much more about the pathophysiology of MwA thanks to the combination of molecular and genetic research with sophisticated neuroimaging techniques like arterial spin labelling (ASL) and fMRI. This review aims to synthesize current knowledge and analyse molecular and neurophysiological targets, providing a foundation for developing targeted therapies to modulate cortical excitability, restore neural network stability, and alleviate the burden of migraine with aura. The most important and impactful research in our field has been the focus of this review, which highlights important developments and their contributions to the knowledge and treatment of migraine with aura.

Specialization of the human hippocampal long axis revisited

The hippocampus possesses anatomical differences along its long axis. Here, we explored the functional specialization of the human hippocampal long axis using network-anchored precision functional MRI in two independent datasets (N = 11 and N = 9) paired with behavioral analysis (N = 266 and N = 238). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus selectively contained a region correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization of the anterior and posterior hippocampal regions using task data identified and replicated a functional double dissociation. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest an unexpected specialization along the long axis of the human hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

Altered Sensorimotor Striatal Network Connectivity in Women With Anorexia Nervosa

OBJECTIVE

Anorexia nervosa (AN) is associated with disturbances in reward processing, cognitive control, and body image perception, implicating striatal dysfunction. Evidence suggests that underweight may modulate brain function in AN. We aimed to investigate whole-brain resting-state functional connectivity (rsFC) of the striatum in patients with AN while controlling for the acute effects of underweight.

METHOD

Using theoretically selected striatal sub-regions, whole-brain rsFC patterns of the striatum were compared among patients with AN (n = 39, BMI = 16.19 ± 1.48 kg/m2), normal weight healthy controls (NHC) (n = 31, BMI = 20.98 ± 1.72 kg/m2), and underweight healthy controls (UHC) (n = 22, BMI = 16.68 ± 0.69 kg/m2). Correlation analysis between rsFC and clinical measures was conducted for the patients with AN.

RESULTS

Compared with the NHC group, AN patients showed increased striatal rsFC with the fronto-parietal network (FPN) and reduced striatal rsFC with sensorimotor and visual regions. Compared with the UHC group, AN patients exhibited reduced striatal rsFC solely with sensorimotor and visual regions. No significant correlations were found between striatal rsFC and clinical variables in the patients with AN.

CONCLUSION

Our findings suggest that decreased striatal rsFC with sensorimotor and visual areas may represent illness-specific neural correlates in patients with AN.

Abnormal functional lateralization and cooperation in bipolar disorder are associated with neurotransmitter and cellular profiles

BACKGROUND

Hemispheric lateralization and cooperation are essential for efficient brain function, and aberrations in both have been found in psychiatric disorders such as schizophrenia. This study investigated alterations in hemispheric lateralization and cooperation among patients with bipolar disorder (BD) and associations with neurotransmitter and cell-type density distributions to identify potential molecular and cellular pathomechanisms.

METHODS

Sixty-seven BD patients and 127 healthy controls (HCs) were examined by resting-state functional MRI (rs-fMRI). Whole-brain maps of the autonomy index (AI) and connectivity between functionally homotopic voxels (CFH) were constructed to reveal BD-specific changes in brain functional lateralization and interhemispheric cooperation, respectively. Spatial associations of regional AI and CFH abnormalities with neurotransmitter and cell-type density distributions were examined by correlation analyses.

RESULTS

Bipolar disorder patients exhibited higher AI values in left superior parietal gyrus, cerebellar right Crus I, and cerebellar right Crus II, and these regional abnormalities were associated with the relative densities (proportions) of oligodendrocyte precursor cells and microglia. Patients also exhibited lower CFH values in right inferior parietal gyrus, bilateral middle occipital gyrus, left postcentral gyrus, and bilateral cerebellar crus II, and these regional abnormalities were associated with the densities of serotonin 1A and dopamine D2 receptors, oligodendrocyte precursor cells, astrocytes, and neurons.

CONCLUSIONS

These findings indicate that abnormal functional lateralization and cooperation in BD with potential molecular and cellular basis.

Association of player position and functional connectivity alterations in collegiate American football players: an fMRI study

Introduction

Resting state-fMRI, provides a sensitive method for detecting changes in brain functional integrity, both with respect to regional oxygenated blood flow and whole network connectivity. The primary goal of this report was to examine alterations in functional connectivity in collegiate American football players after a season of repetitive head impact exposure.

Methods

Collegiate football players completed a rs-fMRI at pre-season and 1 week into post-season. A seed-based functional connectivity method, isolating the posterior cingulate cortex (PCC), was utilized to create individual functional connectivity maps. During group analysis, first, voxel-wise paired sample t-tests identified significant changes in connectivity from pre- to post-season, by player, and previous concussion history. Second, 10 DMN ROIs were constructed by overlaying an anatomical map over regions of positive correlation from one-sample t-tests of pre-season and post-season. These ROIs, plus the LpCun, were included in linear mix-effect modeling, with position or concussion history as covariates.

Results

66 players were included (mean age 20.6 years; 100% male; 34 (51.5%) non-speed position players). The 10 DMN ROIs showed no alterations from pre-season to post-season. By concussion history, the right temporal ROI demonstrated a significant effect on baseline functional connectivity (p = 0.03). Speed players, but not non-speed players, demonstrated a significant decrease in functional connectivity in the precuneus from pre- to post-season (p < 0.001).

Discussion

There are region-specific differences functional connectivity related to both position and concussion history in American collegiate football players. Player position affected functional connectivity across a season of football. Position-specific differences in head impact exposure rate and magnitude plays a crucial role in functional connectivity alterations.

Network Abnormalities in Ischemic Stroke: A Meta-analysis of Resting-State Functional Connectivity

Aberrant large-scale resting-state functional connectivity (rsFC) has been frequently documented in ischemic stroke. However, it remains unclear about the altered patterns of within- and across-network connectivity. The purpose of this meta-analysis was to identify the altered rsFC in patients with ischemic stroke relative to healthy controls, as well as to reveal longitudinal changes of network dysfunctions across acute, subacute, and chronic phases. A total of 24 studies were identified as eligible for inclusion in the present meta-analysis. These studies included 269 foci observed in 58 contrasts (558 patients with ischemic stroke; 526 healthy controls; 38.84% female). The results showed: (1) within-network hypoconnectivity in the sensorimotor network (SMN), default mode network (DMN), frontoparietal network (FPN), and salience network (SN), respectively; (2) across-network hypoconnectivity between the SMN and both of the SN and visual network, and between the FPN and both of the SN and DMN; and (3) across-network hyperconnectivity between the SMN and both of the DMN and FPN, and between the SN and both of the DMN and FPN. Meta-regression showed that hypoconnectivity between the DMN and the FPN became less pronounced as the ischemic stroke phase progressed from the acute to the subacute and chronic phases. This study provides the first meta-analytic evidence of large-scale rsFC dysfunction in ischemic stroke. These dysfunctional biomarkers could help identify patients with ischemic stroke at risk for cognitive, sensory, motor, and emotional impairments and further provide potential insight into developing diagnostic models and therapeutic interventions for rehabilitation and recovery.

Stationarity assessment of resting state condition via permutation entropy on EEG recordings

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is the default condition to characterize brain dynamics. Its successful implementation relies both on the capacity of subjects to comply with the requirement of staying awake while not performing any cognitive task, and on the capacity of the experimenter to validate that compliance. Here we propose a novel approach, based on permutation entropy, to assess the reliability of the resting state hypothesis by evaluating its stability during a recording. We combine the calculation of permutation entropy with a method to estimate its uncertainty out of a single time series. The approach is showcased on electroencephalographic data recorded from young and elderly subjects and considering eyes-closed and eyes-opened resting state conditions. Besides highlighting the reliability of the approach, the results show higher instability in elderly subjects, hinting at qualitative differences between age groups in the distribution of unstable brain activity. The method can be applied to other kinds of electrophysiological data, like magnetoencephalographic recordings. In addition, provided that suitable hardware and software processing units are used, the implementation of the method can be translated into a real time one.

A review of brain structural and functional changes using MRI technology in patients who received bariatric surgery

According to the World Health Organization, obesity is one of the most significant health issues currently because it increases risk for type 2 diabetes and cancer, heart disease, bone health, reproduction, and quality of living and it impacts approximately 500 million adults worldwide. This review analyzed the existing literature focusing on the effects of Metabolic and bariatric surgeries (MBS), including Roux-en-Y gastric bypass and sleeve gastrectomy on changes in brain function and anatomy using magnetic resonance imaging (MRI) technology. A PubMed search using the key words bariatric surgery and MRI conducted in December 2023 resulted in 544 articles. Our literature review identified 24 studies addressing neuroanatomic, neurophysiological, cognitive, and behavioral changes that occurred at different time intervals after different types of bariatric surgery. Our review of the literature found several reports indicating that MBS reverse neuroanatomic alterations and changes in functional connectivity associated with obesity. There were also reported improvements in cognitive performance, memory, executive function, attention, as well as decreased gustatory brain responses to food cues and resting state measures following bariatric surgery. There were instances of improved neural functioning associated with weight loss, suggesting that some neuroanatomic changes can be reversed following weight loss induced by bariatric surgery. Additionally, there were data suggesting that brain connectivity and metabolic health are improved following a bariatric surgical intervention. Together, the existing literature indicates an overall improvement in brain connectivity and health outcomes following bariatric surgery.

ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.

Subregion-specific thalamocortical functional connectivity, executive function, and social behavior in children with autism spectrum disorders

The thalamus has extensive cortical connections and is an integrative hub for cognitive functions governing social behavior. This study examined (1) associations between thalamocortical resting-state functional connectivity (RSFC) and social behavior in children and (2) how various executive function (EF) subdomains mediate the association between thalamocortical RSFC and social behavior. Children from the autism brain imaging data exchange (ABIDE) initiative with neuroimaging, behavioral, and demographic data were included in our study (age < 14, ASD; n = 207, typically developing; n = 259). Thalamocortical RSFC was examined for associations with social communication and interaction (SCI) scores (SRS; social responsiveness scale) using Spearman's rank-order correlation, first in ASD children and then in typically developing children. This was followed by a more granular analysis at the thalamic subregion level. We then examined the mediating roles of eight EF subdomains in ASD children (n = 139). Right thalamus-default mode network (DMN) RSFC was significantly associated with SCI scores in ASD children (ρ = 0.23, pFDR = 0.012), primarily driven by the medial (ρ = 0.22, pFDR = 0.013), ventral (ρ = 0.17, pFDR = 0.036), and intralaminar (ρ = 0.17, pFDR = 0.036) thalamic subregions. Cognitive flexibility (ACME = 0.13, punc = 0.016) and emotional control (ACME = 0.08, punc = 0.020) significantly mediated the association between right thalamus-DMN RSFC and SCI scores. This study provided novel insights into the association between thalamocortical RSFC and social behavior in ASD children at the thalamic subregion level, providing higher levels of precision in brain-behavior mapping. Cognitive flexibility and emotion regulation were highlighted as potential targets to ameliorate the downstream effects of altered thalamocortical connectivity to improve social outcomes in ASD children.

Social behavior in ASD males: The interplay between cognitive flexibility, working memory, and functional connectivity deviations

Autism spectrum disorder (ASD) is highly heterogeneous in presentation. While abnormalities in brain functional connectivity are consistently observed in autistic males, the neurobiological basis underlying the different domains of autism symptoms is unclear. In this study, we evaluated whether individual variations in functional connectivity deviations map to social behavior in ASD males. Using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE), we modeled normative trajectories of between-network resting-state functional connectivity (rsFC) in non-ASD males across childhood (n = 321). These normative charts were then applied to ASD males (n = 418) to calculate individual deviation scores (z-scores) that reflect the degree of rsFC atypicality. Deviations in rsFC patterns among the default mode network (DMN), ventral attention network (VAN), frontoparietal network (FPN), and somatomotor network (SMN) were associated with distinct dimensions of social behavior. Cognitive flexibility and working memory mediated the association between VANxDMN z-scores and social behavioral problems. Our findings underscore the potential of normative models to identify atypical brain connectivity at an individual level, revealing the neurobiological patterns associated with social behavioral problems in ASD that are critical for precision diagnosis and intervention. Social outcomes in ASD males may be improved by targeting cognitive flexibility and working memory.

Transitioning from perceived stress to mental health: The mediating role of self-control in a longitudinal investigation with MRI scans

Background

The neural mechanisms and long-term effects of perceived stress (PS) and self-control (SC) on mental health (MH) are not fully understood. This study seeks to investigate the influence of PS and SC on MH and to identify their neural correlates using fMRI.

Methods

A total of 817 college students participated in behavioral assessments, including the Perceived Stress Scale (PSS), Self-Control Scale (SCS), and Mental Health Continuum Short Form (MHC-SF). Among them, 371 underwent fMRI scans to calculate zfALFF and whole-brain functional connectivity. Additionally, their behavioral measures were reassessed two years later.

Results

Longitudinal behavioral data revealed significant fixed effects of perceived stress and self-control on mental health. Perceived stress significantly predicted decreased mental health at Time 2, and self-control acted as a mediator in such relationship. The results of the behavioral and brain model analyses found that zfALFF in the right temporal region negatively predicted self-control. Functional connectivity between the right temporal region and the right precentral gyrus was also found to negatively predict self-control.

Conclusion

This study highlights the mediating role of self-control in the relationship between perceived stress and mental health. It also identifies specific brain regions and functional connectivity associated with self-control, providing new neurobiological evidence for mental health interventions.