Most recent paper

Front Neurosci. 2025 Mar 5;19:1444999. doi: 10.3389/fnins.2025.1444999. eCollection 2025.

ABSTRACT

INTRODUCTION: Brain dynamics offer a more direct insight into brain function than network structure, providing a profound understanding of dysregulation and control mechanisms within intricate brain systems. This study investigates the dynamics of functional brain networks in major depressive disorder (MDD) patients to decipher the mechanisms underlying brain dysfunction during depression and assess the impact of repetitive transcranial magnetic stimulation (rTMS) intervention.

METHODS: We employed energy landscape analysis of functional magnetic resonance imaging (fMRI) data to examine the dynamics of functional brain networks in MDD patients. The analysis focused on key dynamical indicators of the default mode network (DMN), the salience network (SN), and the central execution network (CEN). The effects of rTMS intervention on these networks were also evaluated.

RESULTS: Our findings revealed notable dynamical alterations in the pDMN, the vDMN, and the aSN, suggesting their potential as diagnostic and therapeutic markers. Particularly striking was the altered activity observed in the dDMN in the MDD group, indicative of patterns associated with depressive rumination. Notably, rTMS intervention partially reverses the identified dynamical alterations.

DISCUSSION: Our results shed light on the intrinsic dysfunction mechanisms of MDD from a dynamic standpoint and highlight the effects of rTMS intervention. The identified alterations in brain network dynamics provide promising analytical markers for the diagnosis and treatment of MDD. Future studies should further explore the clinical applications of these markers and the comprehensive dynamical effects of rTMS intervention.

PMID:40109660 | PMC:PMC11920141 | DOI:10.3389/fnins.2025.1444999

Int J Radiat Oncol Biol Phys. 2025 Mar 17:S0360-3016(25)00247-0. doi: 10.1016/j.ijrobp.2025.03.017. Online ahead of print.

ABSTRACT

BACKGROUND: This prospective observational study employed resting-state functional MRI (rs-fMRI) to investigate network-level disturbances associated with neurocognitive function (NCF) changes in patients with gliomas following partial-brain radiation therapy (RT).

METHODS: Adult post-operative patients with either IDH-wildtype or IDH-mutant gliomas underwent computerized NCF testing and rs-fMRI at baseline and 6 months post-RT. rs-fMRI data were assessed using seed-based functional connectivity (FC). NCF changes were quantified by the percent change in age-normalized composite scores from baseline (ΔNCFcomp). Connectivity-regression analysis assessed the association between network FC changes and NCF changes, using a split-sample approach with a 26-patient training set and a 6-patient validation set, iterated 200 times. Permutation tests evaluated the significance of network selection.

RESULTS: Between September 2020 and December 2023, 43 patients were enrolled, with 32 completing both baseline and follow-up evaluations. The mean ΔNCFcomp was 2.9% (SD: 13.7%), with 38% experiencing a decline. Patients with IDH-mutant glioma had similar NCF changes compared to those with IDH-wildtype glioma. Intra-hemispheric FC was similar between ipsilateral and contralateral hemispheres for 91% patients at baseline, and 69% had similar intra-hemispheric FC change post-treatment. FC changes accounted for a moderate fraction of variance in NCF changes (mean R2: 0.301, SD: 0.249), with intra-network FC of the Parietal Memory Network (PMN-PMN, p=0.001) and inter-network FC between the PMN and the Visual Network (PMN-VN, p=0.002) as the most significant factors. Similar findings were obtained by sensitivity analyses using only the FC data from the hemisphere contralateral to the tumor.

CONCLUSIONS: Post-RT rs-fMRI changes significantly reflected NCF decline, highlighting rs-fMRI as a promising imaging biomarker for neurocognitive decline after RT.

PMID:40107623 | DOI:10.1016/j.ijrobp.2025.03.017

Neurologist. 2025 Mar 19. doi: 10.1097/NRL.0000000000000622. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore the effects of Intermittent Theta Burst Stimulation (iTBS) on the posterior inferior frontal gyrus of the left hemisphere on the expression function of patients with aphasia after stroke, and to explore the specific mechanism of fractional amplitude of low-frequency fluctuation (fALFF) analysis and degree centrality (DC) analysis of resting-state functional MRI.

METHODS: According to the inclusion and exclusion criteria, 40 patients with poststroke aphasia were randomized into a treatment group (iTBS group) and a control group (S-iTBS group). Patients in the iTBS group received iTBS +speech training, and patients in the S-iTBS group received sham iTBS + speech training. The Western aphasia test (Chinese version) was used to assess spontaneous language, naming, retelling, and aphasia quotient before and after treatment; resting-state fMRI scans were performed before and after treatment, and the scanned image data were analyzed to explore specific activated or suppressed brain regions.

RESULTS: Compared with before and after treatment, the scores of spontaneous language, naming, retelling, and aphasia quotient of the patients in iTBS group improved significantly, and the spontaneous language, naming, retelling, and aphasia quotient of the patients in S-iTBS group also improved. After the treatment, the scores of naming, retelling and aphasia quotient of the patients in the iTBS group improved significantly compared with that of the patients in the S-iTBS group. The resting-state fMRI results of the 2 groups before and after treatment were fALFF analysis found that the fALFF value increased in multiple brain regions in the left frontal and temporal lobes of the patients in iTBS group. Meanwhile, DC analysis also found increased DC values in multiple frontotemporal brain regions of the left hemisphere of patients in the iTBS group, indicating that the improved activation of the above brain regions of the patients in the iTBS group was significantly compared with that of the patients in the S-iTBS group.

CONCLUSIONS: iTBS combined with conventional speech training significantly improved the expression function of patients with aphasia after stroke. After iTBS action on the left hemisphere, increased activation of multiple brain regions in the left hemisphere may be one of the important mechanisms by which iTBS improves expression function in poststroke aphasia patients.

PMID:40104992 | DOI:10.1097/NRL.0000000000000622

Int J Ophthalmol. 2025 Mar 18;18(3):487-495. doi: 10.18240/ijo.2025.03.16. eCollection 2025.

ABSTRACT

AIM: To investigate changes in local brain activity after laser assisted in situ keratomileusis (LASIK) in myopia patients, and further explore whether post-LASIK (POL) patients and healthy controls (HCs) can be distinguished by differences in dynamic amplitude of low-frequency fluctuations (dALFF) in specific brain regions.

METHODS: The resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 15 myopic patients who underwent LASIK and 15 matched healthy controls. This method was selected to calculate the corresponding dALFF values of each participant, to compare dALFF between the groups and to determine whether dALFF distinguishes reliably between myopic patients after LASIK and HCs using the linear support vector machine (SVM) permutation test (5000 repetitions).

RESULTS: dALFF was lower in POL than in HCs at the right precentral gyrus and right insula. Classification accuracy of the SVM was 89.1% (P<0.001).

CONCLUSION: The activity of spontaneous neurons in the right precentral gyrus and right insula of myopic patients change significantly after LASIK. SVM can correctly classify POL patients and HCs based on dALFF differences.

PMID:40103964 | PMC:PMC11865659 | DOI:10.18240/ijo.2025.03.16

Int J Ophthalmol. 2025 Mar 18;18(3):469-477. doi: 10.18240/ijo.2025.03.14. eCollection 2025.

ABSTRACT

AIM: To assess the alterations in the resting-state function connections between the two cerebral hemispheres in patients with optic neuritis (ON) and healthy controls (HCs).

METHODS: A total of 12 ON patients (six males and six females) and 12 HCs (six males and six females) who were highly matched for sex, age, and educational level were recruited. They underwent functional magnetic resonance imaging (fMRI), testing and brain activities were assessed using the degree centrality (DC) method. Correlation analysis between the mean DC values in specific brain areas and behavior performances was analyzed as well. Linear correlations between A anxiety scale (AS) and depression scale (DS) values and DC values in brain regions of patients with ON were also analyzed.

RESULTS: The areas that showed a higher DC value in ON patients were the right angular gyrus and bilateral precuneus, while the left insula and left superior temporal gyrus (LSTG) were regions that presented a lower DC value in ON patients. A receiver operating characteristic (ROC) curve analysis confirmed the accuracy of the area under the curve (AUC) assessment. Linear analysis showed anxiety scale (AS) and depression scale (DS) values in the left insula were both negatively correlated with DC values, while best corrected visual acuity logMAR-R (BCVA logMAR-R) showed a negative correlation with DC in the LSTG.

CONCLUSION: The study explores altered brain activities of specific regions in patients with ON. The results provide clues for revealing the underlying mechanism of ON development.

PMID:40103952 | PMC:PMC11865650 | DOI:10.18240/ijo.2025.03.14

Cereb Cortex. 2025 Mar 6;35(3):bhaf063. doi: 10.1093/cercor/bhaf063.

ABSTRACT

Accumulating evidence suggests that individuals with high sensory processing sensitivity often experience sensory overload and have difficulty sustaining attention, which can particularly resemble attention deficit symptoms of attention-deficit/hyperactivity disorder. However, due to the lack of understanding about the potential neural pathways involved in those processes, a comprehensive view of how sensory processing sensitivity and attention deficit are related is generally limited. Here, we quantified the sensory processing sensitivity and attention deficit using the Highly Sensitive Person Scale and the Adult Attention-deficit/Hyperactivity Disorder Self-Report Scale, respectively, to investigate the association between sensory processing sensitivity and attention deficit and further identify the corresponding neural substrates via the use of resting-state functional Magnetic Resonance Imaging (fMRI) analyses. On the behavioral level, the results indicated a significantly positive correlation between sensory processing sensitivity and attention deficit traits, while on the neural level, the sensory processing sensitivity score was positively correlated with functional connectivity between the rostral hippocampus and inferior parietal lobule, which is the core regions of the attention network. Mediation analysis revealed that hippocampus-Inferior Parietal Lobule (IPL) connectivity can further influence attention deficit through a mediating role of sensory processing sensitivity. Overall, these findings suggest that enhanced functional coupling between the hippocampus and attention network regions may heighten sensitivity to environmental stimuli, leading to increased distractibility and potentially contributing to attention deficit.

PMID:40103362 | DOI:10.1093/cercor/bhaf063

Neuroimage. 2025 Mar 16:121155. doi: 10.1016/j.neuroimage.2025.121155. Online ahead of print.

ABSTRACT

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular) was higher in older adults with higher physical function compared to older adults with lower physical function. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. The interaction of left sensorimotor network segregation and age groups was associated with higher working memory function. Higher left sensorimotor, left vestibular, right anterior cingulate cortex, and interaction of left anterior cingulate cortex network segregation and age groups were associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

PMID:40101865 | DOI:10.1016/j.neuroimage.2025.121155

PLoS One. 2025 Mar 18;20(3):e0319746. doi: 10.1371/journal.pone.0319746. eCollection 2025.

ABSTRACT

Functional connectomics is a popular approach to investigate the neural underpinnings of developmental disorders of which attention deficit hyperactivity disorder (ADHD) is one of the most prevalent. Nonetheless, neuronal mechanisms driving the aberrant functional connectivity resulting in ADHD symptoms remain largely unclear. Whereas resting state activity reflecting intrinsic tonic background activity is only vaguely connected to behavioral effects, naturalistic neuroscience has provided means to measure phasic brain dynamics associated with overt manifestation of the symptoms. Here we collected functional magnetic resonance imaging (fMRI) data in three experimental conditions, an active virtual reality (VR) task where the participants execute goal-directed behaviors, a passive naturalistic Video Viewing task, and a standard Resting State condition. Thirty-nine children with ADHD and thirty-seven typically developing (TD) children participated in this preregistered study. Functional connectivity was examined with network-based statistics (NBS) and graph theoretical metrics. During the naturalistic VR task, the ADHD group showed weaker task performance and stronger functional connectivity than the TD group. Group differences in functional connectivity were observed in widespread brain networks: particularly subcortical areas showed hyperconnectivity in ADHD. More restricted group differences in functional connectivity were observed during the Video Viewing, and there were no group differences in functional connectivity in the Resting State condition. These observations were consistent across NBS and graph theoretical analyses, although NBS revealed more pronounced group differences. Furthermore, during the VR task and Video Viewing, functional connectivity in TD controls was associated with task performance during the measurement, while Resting State activity in TD controls was correlated with ADHD symptoms rated over six months. We conclude that overt expression of the symptoms is correlated with aberrant brain connectivity in ADHD. Furthermore, naturalistic paradigms where clinical markers can be coupled with simultaneously occurring brain activity may further increase the interpretability of psychiatric neuroimaging findings.

PMID:40100891 | DOI:10.1371/journal.pone.0319746

Front Aging Neurosci. 2025 Mar 3;17:1499262. doi: 10.3389/fnagi.2025.1499262. eCollection 2025.

ABSTRACT

BACKGROUND: Chronic pain is a common non-motor symptom of Parkinson's disease (PD) that significantly impacts patients' quality of life, but its neural mechanisms remain poorly understood. This study investigated changes in spontaneous neuronal activity and functional connectivity (FC) associated with chronic pain in PD patients.

METHODS: The study included 41 PD patients with chronic pain (PDP), 41 PD patients without pain (nPDP), and 29 healthy controls. Pain severity was assessed using the visual analog scale (VAS). Resting-state fMRI images were used to measure the amplitude of low-frequency fluctuations (ALFF) as an indicator of regional brain activity. Subsequently, FC analysis was performed to evaluate synchronization between ALFF-identified regions and the entire brain.

RESULTS: Compared to nPDP patients, PDP patients exhibited decreased ALFF in the right putamen, and increased ALFF in motor regions, including the right superior frontal gyrus/supplementary motor area and the left paracentral lobule/primary motor cortex. Additionally, PDP patients exhibited diminished right putamen-based FC in the midbrain, anterior cingulate cortex, orbitofrontal cortex, middle frontal gyrus, middle temporal gyrus, and posterior cerebellar lobe. The correlation analysis revealed that ALFF values in the right putamen were negatively associated with VAS scores in PDP patients.

CONCLUSION: This study demonstrates that chronic pain in PD is associated with reduced ALFF in the putamen and disrupted FC with brain regions involved in pain perception and modulation, highlighting the critical role of dopaminergic degeneration in the development and maintenance of pain in PD.

PMID:40099248 | PMC:PMC11911387 | DOI:10.3389/fnagi.2025.1499262

Ann Gen Psychiatry. 2025 Mar 17;24(1):14. doi: 10.1186/s12991-025-00553-1.

ABSTRACT

BACKGROUND: The growing popularity of internet gaming among adolescents and young adults has driven an increase in both casual and excessive gaming behavior. Nevertheless, it remains unclear how progressive increases in internet gaming engagement led to changes within and between brain networks. This study aims to investigate these connectivity alterations across varying levels of gaming involvement.

METHODS: In this cross-sectional study, 231 participants were recruited and classified into three groups according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for Internet Gaming Disorder (IGD): IGD group, highly engaged gaming(HEG) group, and lowly engaged gaming (LEG) group. Resting-state fMRI data from 217 participants (143 males, 74 females) were included in the final analysis. Independent component analysis was used to examine differences in intra- and inter-network functional connectivity (FC)across the three groups.

RESULTS: No significant differences were found in intra-network FC across the three groups. However, significant inter-network differences between the dorsal attention network(dAN)and the visual network (VN) among the three groups were observed. The HEG group exhibited significantly higher dAN-VN functional network connectivity (FNC) compared to the LEG group. Linear correlation analyses showed no significant correlation between the dAN-VN FNC values and IGD-20T scores.

CONCLUSION: Throughout the development of IGD, increasing levels of engagement are associated with a rise and subsequent decline in FNC of DAN-VN. This pattern may reflect top-down attentional regulation in the early stages of addiction, followed by attentional bias as addiction progresses.

PMID:40098002 | DOI:10.1186/s12991-025-00553-1

Transl Psychiatry. 2025 Mar 17;15(1):86. doi: 10.1038/s41398-025-03265-y.

ABSTRACT

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21-23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

PMID:40097427 | DOI:10.1038/s41398-025-03265-y

Cold Spring Harb Perspect Med. 2025 Mar 17:a041841. doi: 10.1101/cshperspect.a041841. Online ahead of print.

ABSTRACT

Functional neuroimaging techniques are increasingly being used to advance the diagnosis and management of Parkinson's disease (PD). Methods such as [18F]-fluorodeoxyglucose positron emission tomography (FDG PET), resting-state functional magnetic resonance imaging (rs-fMRI), arterial spin labeling (ASL) MRI, and single-photon emission computed tomography (SPECT) enable the identification of disease-specific patterns like the PD-related pattern (PDRP) and PD cognition-related pattern (PDCP), which correlate with motor and cognitive symptoms. Network analysis using graph theory further elucidates the alterations in brain connectivity associated with PD, providing insights into disease progression and response to treatment. Moreover, these neuroimaging patterns assist in distinguishing PD from atypical parkinsonian syndromes, enhancing diagnostic accuracy. Understanding the impact of genetic variants like LRRK2 and GBA1 on functional connectivity highlights the potential for precision medicine in PD. As neuroimaging technologies evolve, their integration into clinical practice will be pivotal in the personalized management of PD, offering improved diagnostic precision and targeted therapeutic interventions.

PMID:40097189 | DOI:10.1101/cshperspect.a041841

Hum Brain Mapp. 2025 Apr 1;46(5):e70193. doi: 10.1002/hbm.70193.

ABSTRACT

Delineating the functional organization of mesoscale cortical columnar structure is essential for understanding brain function. We have previously demonstrated a high spatial correspondence between BOLD fMRI and LFP responses to tactile stimuli in the primary somatosensory cortex area 3b of nonhuman primates. This study aims to explore how 2D spatial profiles of the functional column vary across cortical layers (defined by three cortical depths) in both tactile stimulation and resting states using fMRI. At 9.4 T, we acquired submillimeter-resolution oblique fMRI data from cortical areas 3b and 1 of anesthetized squirrel monkeys and obtained fMRI signals from three cortical layers. In both areas 3b and 1, the tactile stimulus-evoked fMRI activation foci were fitted with point spread functions (PSFs), from which shape parameters, including full width at half maximum (FWHM), were derived. Seed-based resting-state fMRI data analysis was then performed to measure the spatial profiles of resting-state connectivity within and between areas 3b and 1. We found that the tactile-evoked fMRI response and local resting-state functional connectivity were elongated at the superficial layer, with the major axes oriented in lateral to medial (from digit 1 to digit 5) direction. This elongation was significantly reduced in the deeper (middle and bottom) layers. To assess the robustness of these spatial profiles in distinguishing cortical layers, shape parameters describing the spatial extents of activation and resting-state connectivity profiles were used to classify the layers via self-organizing maps (SOM). A minimal overall classification error (~13%) was achieved, effectively classifying the layers into two groups: the superficial layer exhibited distinct features from the two deeper layers in the rsfMRI data. Our results support distinct 2D spatial profiles for superficial versus deeper cortical layers and reveal similarities between stimulus-evoked and resting-state configurations.

PMID:40095731 | DOI:10.1002/hbm.70193

J Alzheimers Dis. 2025 Mar 17:13872877251317489. doi: 10.1177/13872877251317489. Online ahead of print.

ABSTRACT

BackgroundGenetic susceptibility is a primary factor contributing to etiology of late-onset Alzheimer's disease (LOAD). The exact mechanisms and timeline through which APOE/PICALM influence brain functions and contribute to LOAD remain unidentified. This includes their effects on individuals prior to the development of the disease.ObjectiveTo investigate the effects of APOE and PICALM risk genes on brain health and function in non-demented individuals. This study aims to differentiate the combined risk effects of both genes from the risk associated solely with APOE, and to examine how PICALM alleles influence the risk linked to APOE.MethodsAPOE/PICALM alleles were assessed to determine the genetic risk of LOAD in 79 healthy, middle-aged participants who underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The resting-state signal was analyzed to estimate relative spectral power, complexity (Higuchi's algorithm), and connectivity (coherence in EEG and independent component analysis-based connectivity in fMRI).ResultsThe main findings indicated that individuals at risk for LOAD exhibited reduced signal complexity and the so-called "slowing of EEG" which are well-known EEG markers of Alzheimer's disease. Additionally, these individuals showed altered functional connectivity in fMRI (within attention-related areas).ConclusionsRisk alleles of APOE/PICALM may affect brain integrity and function prior to the clinical onset of the disease.

PMID:40095677 | DOI:10.1177/13872877251317489

medRxiv [Preprint]. 2025 Mar 3:2025.02.27.25322899. doi: 10.1101/2025.02.27.25322899.

ABSTRACT

Resting-state fMRI (rs-fMRI) captures brain activity at rest, it demonstrates information on how different regions interact without explicity task-based influences. This provides insights into both healthy and disordered brain states. However, clinical application of rs-fMRI remains challenging due to the wide variability in functional connectivity across individuals. Traditional data-driven methods like independent component analysis (ICA) struggle to balance these individual differences with broader patterns. Constrained methods, such as constrained ICA (cICA), have been introduced to address this by integrating templates from multiple external datasets to enhance accuracy and consistency. In our study, we analyzed rs-fMRI data from 100,517 individuals from diverse datasets, processed through a robust quality-control dynamic connectivity pipeline established in previous work. Using the resulting brain state templates as cICA priors, we examined the effectiveness of cICA for schizophrenia classification using a combined CNN and LSTM architecture. Results showed stable classification accuracy (87.6% to 86.43%) for the CNN model, while the LSTM model performed less optimally, likely due to sequence processing, yet still yielded comparable results. These findings underscore the potential of group-informed methods and prior data templates in constrained dynamic ICA, offering improved reliability and clinical relevance in rs-fMRI analysis and advancing our understanding of brain function.

PMID:40093229 | PMC:PMC11908281 | DOI:10.1101/2025.02.27.25322899

medRxiv [Preprint]. 2025 Mar 5:2025.03.02.25322248. doi: 10.1101/2025.03.02.25322248.

ABSTRACT

OBJECTIVE: Determine whether acute behavioral, electroencephalography (EEG), and functional MRI (fMRI) biomarkers of consciousness are associated with outcome after severe traumatic brain injury (TBI).

METHODS: Patients with acute severe TBI admitted consecutively to the intensive care unit (ICU) participated in a multimodal battery assessing behavioral level of consciousness (Coma Recovery Scale-Revised [CRS-R]), cognitive motor dissociation (CMD; task-based EEG and fMRI), covert cortical processing (CCP; stimulus-based EEG and fMRI), and default mode network connectivity (DMN; resting-state fMRI). The primary outcome was 6-month Disability Rating Scale (DRS) total scores.

RESULTS: We enrolled 55 patients with acute severe TBI. Six-month outcome was available in 45 (45.2±20.7 years old, 70% male), of whom 10 died, all due to withdrawal of life-sustaining treatment (WLST). Behavioral level of consciousness and presence of command-following in the ICU were each associated with lower (i.e., better) DRS scores (p=0.003, p=0.011). EEG and fMRI biomarkers did not strengthen this relationship, but higher DMN connectivity was associated with better recovery on multiple secondary outcome measures. In a subsample of participants without command-following on the CRS-R, CMD (EEG:18%; fMRI:33%) and CCP (EEG:91%; fMRI:79%) were not associated with outcome, an unexpected result that may reflect the high rate of WLST. However, higher DMN connectivity was associated with lower DRS scores (ρ[95%CI]=-0.41[-0.707, -0.027]; p=0.046) in this group.

INTERPRETATION: Standardized behavioral assessment in the ICU may improve prediction of recovery from severe TBI. Further research is required to determine whether integrating behavioral, EEG, and fMRI biomarkers of consciousness is more predictive than behavioral assessment alone.

PMID:40093212 | PMC:PMC11908294 | DOI:10.1101/2025.03.02.25322248

bioRxiv [Preprint]. 2025 Mar 5:2025.03.04.641400. doi: 10.1101/2025.03.04.641400.

ABSTRACT

It is frequently assumed that the phenotypic heterogeneity in autism spectrum disorder reflects underlying pathobiological variation. However, direct evidence in support of this hypothesis is lacking. Here, we leverage cross-species functional neuroimaging to examine whether variability in brain functional connectivity reflects distinct biological mechanisms. We find that fMRI connectivity alterations in 20 distinct mouse models of autism (n=549 individual mice) can be clustered into two prominent hypo- and hyperconnectivity subtypes. We show that these connectivity profiles are linked to distinct signaling pathways, with hypoconnectivity being associated with synaptic dysfunction, and hyperconnectivity reflecting transcriptional and immune-related alterations. Extending these findings to humans, we identify analogous hypo- and hyperconnectivity subtypes in a large, multicenter resting state fMRI dataset of n=940 autistic and n=1036 neurotypical individuals. Remarkably, hypo- and hyperconnectivity autism subtypes are replicable across independent cohorts (accounting for 25.1% of all autism data), exhibit distinct functional network architecture, are behaviorally dissociable, and recapitulate synaptic and immune mechanisms identified in corresponding mouse subtypes. Our cross-species investigation, thus, decodes the heterogeneity of fMRI connectivity in autism into distinct pathway-specific etiologies, offering a new empirical framework for targeted subtyping of autism.

PMID:40093106 | PMC:PMC11908180 | DOI:10.1101/2025.03.04.641400

Psychol Med. 2025 Mar 17;55:e88. doi: 10.1017/S0033291725000510.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is marked by significant changes to the local synchrony of spontaneous neural activity across various brain regions. However, many methods for assessing this local connectivity use fixed or arbitrary neighborhood sizes, resulting in a decreased capacity to capture smooth changes to the spatial gradient of local correlations. A newly developed method sensitive to classical anatomo-functional boundaries, Iso-Distant Average Correlation (IDAC), was therefore used to examine depression associated alterations to the local functional connectivity of the brain.

METHOD: One-hundred and forty-seven adolescents and young adults with MDD and 94 healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI) scan. Whole-brain functional connectivity maps of intracortical neural activity within iso-distant local areas (5-10, 15-20, and 25-30 mm) were generated to characterize local fMRI signal similarities.

RESULTS: Across all spatial distances, MDD participants demonstrated greater local functional connectivity of the bilateral posterior hippocampus, retrosplenial cortex, dorsal insula, fusiform gyrus, and supplementary motor area. Local connectivity alterations in short and medium distances (5-10 and 15-20 mm) in the mid insula cortex were additionally associated with expressive suppression use, independent of depressive symptom severity.

CONCLUSIONS: Our study identified increased synchrony of the neural activity in several regions commonly implicated in the neurobiology of depression. These effects were relatively consistent across the three distances examined. Longitudinal investigation of this altered local connectivity will clarify whether these differences are also found in other age groups and if this relationship is modified by increased disease chronicity.

PMID:40091390 | DOI:10.1017/S0033291725000510

Commun Biol. 2025 Mar 16;8(1):447. doi: 10.1038/s42003-025-07889-0.

ABSTRACT

Resting-state functional magnetic resonance imaging (fMRI) is used to derive functional connectivity (FC) between brain regions. Typically, blood oxygen level-dependent (BOLD) contrast is used. However, BOLD's reliance on neurovascular coupling poses challenges in reflecting brain activity accurately, leading to reduced sensitivity in white matter (WM). WM BOLD signals have long been considered physiological noise, although recent evidence shows that both stimulus-evoked and resting-state WM BOLD signals resemble those in gray matter (GM), albeit smaller in amplitude. We introduce apparent diffusion coefficient fMRI (ADC-fMRI) as a promising functional contrast for GM and WM FC, capturing activity-driven neuromorphological fluctuations. Our study compares BOLD-fMRI and ADC-fMRI FC in GM and WM, showing that ADC-fMRI mirrors BOLD-fMRI connectivity in GM, while capturing more robust FC in WM. ADC-fMRI displays higher average clustering and average node strength in WM, and higher inter-subject similarity, compared to BOLD. Taken together, this suggests that ADC-fMRI is a reliable tool for exploring FC that incorporates gray and white matter nodes in a novel way.

PMID:40091123 | DOI:10.1038/s42003-025-07889-0

Neurosci Biobehav Rev. 2025 Mar 14:106106. doi: 10.1016/j.neubiorev.2025.106106. Online ahead of print.

ABSTRACT

Metastability is proposed as the mechanism supporting our adaptive responses to the environment. While extensive research has characterized brain metastability during rest and task performance, prior studies have mainly focused on understanding underlying mechanisms, with limited exploration of its application in mental processes and behaviors. This scoping review offers an overview of the existing empirical literature in this area. Through a systematic search that included 36 articles, our results reveal a predominance of resting-state fMRI studies, variability in how metastability is defined, and a lack of consideration for common confounds in neuroimaging data. The review concludes with suggestions for future research directions to address crucial unresolved issues in the field.

PMID:40090532 | DOI:10.1016/j.neubiorev.2025.106106