Most recent paper

J Atten Disord. 2025 Feb 10:10870547251315230. doi: 10.1177/10870547251315230. Online ahead of print.

ABSTRACT

OBJECTIVE: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine the extent to which ADHD and autism could be effectively distinguished via machine learning from one another on this basis and (b) to identify the brain networks differentially implicated in the two conditions.

METHOD: Data from two publicly available resting-state functional magnetic resonance imaging (fMRI) resources-Autism Brain Imaging Data Exchange (ABIDE) and the ADHD-200 Consortium-were analyzed. A total of 330 participants (65 females and 265 males; mean age = 11.6 years), comprising equal subgroups of 110 participants each for ADHD, autism, and healthy controls (HC), were selected from the data sets ensuring data quality and the exclusion of comorbidities. We identified region-to-region connectivity values, which were subsequently employed as inputs to the linear discriminant analysis algorithm.

RESULTS: Machine learning models provided strong differentiation between connectivity patterns in participants with ADHD and autism-with the highest accuracy of 85%. Predominantly frontoparietal network alterations in connectivity discriminate ADHD individuals from autism and neurotypical group. Networks contributing to discrimination of autistic individuals from neurotypical group were more heterogeneous. These included language, salience, and frontoparietal networks.

CONCLUSION: These results contribute to our understanding of the distinct neural signatures underlying ADHD and autism in terms of intrinsic patterns of brain connectivity. The high level of discriminability between ADHD and autism, highlights the potential role of brain based metrics in supporting differential diagnostics.

PMID:39927595 | DOI:10.1177/10870547251315230

J Pain Res. 2025 Feb 4;18:579-587. doi: 10.2147/JPR.S500924. eCollection 2025.

ABSTRACT

PURPOSE: The objective of this research is to delve into the central pathological mechanisms involved in the transformation from acute to chronic pain.

PATIENTS AND METHODS: This study enrolled 86 individuals with acute neck pain and 89 with chronic neck pain. Utilizing a 3.0T MR scanner, we obtained three-dimensional T1-weighted imaging (3D-T1WI) images and analyzed structural differences between the two groups with Freesurfer software to evaluate alterations in cortical thickness. Additionally, Blood Oxygen Level-Dependent functional Magnetic Resonance Imaging (BOLD-fMRI) images were acquired to assess intergroup differences in low-frequency amplitude using DPARSF software.

RESULTS: Chronic neck pain patients exhibited increased cortical thickness in the left rostral middle frontal, left isthmus cingulate, left superior frontal, and right precuneus regions compared to those with acute neck pain. Low-frequency amplitude measures revealed decreased activity in the left dorsolateral superior frontal gyrus and left postcentral gyrus, among other areas, and increased activity in the right middle frontal gyrus and the opercular part of the right inferior frontal gyrus.

CONCLUSION: Our findings indicate that dysfunction and structural changes in the limbic system and prefrontal cortex may play a pivotal role in the progression from acute to chronic neck pain. These insights provide a significant new direction for understanding the central mechanisms underlying pain chronicity.

PMID:39926191 | PMC:PMC11806704 | DOI:10.2147/JPR.S500924

Front Hum Neurosci. 2025 Jan 24;19:1481474. doi: 10.3389/fnhum.2025.1481474. eCollection 2025.

ABSTRACT

INTRODUCTION: Mild-to-moderate traumatic brain injury (mmTBI) that lead to deficits in balance and gait are difficult to resolve through standard therapy protocols, and these deficits can severely impact a patient's quality of life. Recently, translingual neural stimulation (TLNS) has emerged as a potential therapy for mmTBI-related balance and gait deficits by inducing neuroplastic changes in the brain gray matter structure. However, it is still unclear how interactions within and between functional networks in brain are affected by TLNS. The current study aimed to extend our previous resting-state functional connectivity (RSFC) study investigating the effects of TLNS intervention on outcome measures related to gait and balance.

METHODS: An experimental PoNS device was utilized to deliver the TLNS. The 2-week TLNS intervention program, specifically stimulation during focused physical therapy focused on recovery of gait and balance, included twice-daily treatment in the laboratory and the same program at home during the intervening weekend. The resting-state fMRI datasets at pre- and post-interventions were collected by 3T MRI scanner with nine mmTBI patients. All participants also received both Sensory Organization Test (SOT) and Dynamic Gait Index (DGI) testing pre- and post-intervention as part of the behavioral assessment.

RESULTS: Compared to baseline, TLNS intervention led to statistically significant improvements in both the SOT [t (8) = 2.742, p = 0.028] and the DGI [t (8) = 2.855, p = 0.024] scores. Moreover, significant increases in intra- and inter-network RSFC were observed, particularly within the visual, default mode, dorsal attention, frontoparietal (FPN), and somatosensory (SMN) networks. Additionally, there were significant correlations between the SOT and inter-network FC [between FPN and SMN, r (9) = -0.784, p = 0.012] and between the DGI and intra-network FC [within SMN, r (9) = 0.728, p = 0.026].

DISCUSSION: These findings suggest that TLNS intervention is an effective in increasing somatosensory processing, vestibular-visual interaction, executive control and flexible shifting, and TLNS may be an effective approach to inducing brain network plasticity and may serve as a potential therapy for mmTBI-related gait and balance deficits.

PMID:39925723 | PMC:PMC11802553 | DOI:10.3389/fnhum.2025.1481474

Acta Radiol. 2025 Feb 9:2841851241310398. doi: 10.1177/02841851241310398. Online ahead of print.

ABSTRACT

BACKGROUND: Primary insomnia (PI) is one of the most common sleep disorders. Diagnosis of insomnia is mainly based on subjective sleep difficulties, and it is still necessary to find objective neurobiological markers.

PURPOSE: To investigate the functional connectivity (FC) of frontal hub regions important for PI.

MATERIAL AND METHODS: We enrolled 20 patients (5 men, 15 women) with PI and 20 controls (5 men, 15 women), matching age, sex. We used resting-state functional magnetic resonance imaging (fMRI) and voxel-mirrored homotopic connectivity (VMHC) to analyze the abnormal changes of FC in the frontal lobe of PI patients.

RESULTS: Compared to controls, abnormal FC regions were mainly concentrated in the superior frontal gyrus (L/R), middle frontal gyrus (L/R), and inferior frontal gyrus (L) of the orbital region and the inferior frontal gyrus of the opercular region (L) (P < 0.05). The VMHC results showed abnormal FC in the middle frontal gyrus of the orbital region (GFR correction, voxel P < 0.01, cluster P < 0.025) in PI patients. The FC between the orbitofrontal gyrus and the inferior frontal gyrus of the opercular region with the frontal gyrus of the medial orbital region demonstrated a significant correlation with the clinical scale (p < 0.05).

CONCLUSION: Our study identified abnormal FC, which was mainly located in the orbitofrontal gyrus and the inferior frontal gyrus of the opercular region, in the frontal lobe of patients with insomnia using resting-state fMRI. This is helpful to understand the abnormal neural activity mechanism of insomnia in the frontal lobe and provide a relatively accurate brain region basis for future prevention, diagnosis, and treatment.

PMID:39925044 | DOI:10.1177/02841851241310398

Brain Behav. 2025 Feb;15(2):e70302. doi: 10.1002/brb3.70302.

ABSTRACT

PURPOSE: The cerebellum's role extends beyond motor control, impacting various cognitive functions. A growing body of evidence supports the idea that the cerebellum optimizes performance across cognitive domains, suggesting critical connectivity with the neocortex. This study investigates how cerebellar transcranial direct current stimulation (tDCS) targeting the right Crus II region modulates functional brain connectivity.

METHOD: Using a within-subject design, 21 healthy participants underwent both sham and anodal cerebellar tDCS at 2 mA during 20 min of concurrent resting-state fMRI sessions. Data was preprocessed, and connectivity changes were examined using seed-to-voxel analysis. Given the potential impact of cerebellar dysfunctions on symptoms associated with autism spectrum disorders, we also assessed how individual autism quotient (AQ) scores might influence cerebellar functional connectivity. Moreover, electrical field simulations were computed for each participant to explore the effects of individual differences.

FINDINGS: Results indicated increased functional connectivity between the cerebellar Crus II and the right inferior frontal gyrus (IFG) during active tDCS compared to sham stimulation. The IFG (part of the Action Observation Network) plays a crucial role in understanding the actions and intentions of others, implicating the cerebellum in higher-order cognitive processes. In addition, linear mixed-effects models revealed an interaction between electric field strength and AQ scores, suggesting that functional connectivity changes are based on individual psychobiological differences.

CONCLUSION: Cerebellar tDCS significantly altered functional brain connectivity, particularly between the cerebellar Crus II and the IFG, both involved in social cognition. These findings contribute to our understanding of the cerebellum's role beyond motor control, highlighting its impact on cognitive and social processes and its potential for therapeutic applications, such as autism spectrum disorders.

PMID:39924992 | PMC:PMC11808187 | DOI:10.1002/brb3.70302

Mol Autism. 2025 Feb 8;16(1):8. doi: 10.1186/s13229-024-00631-3.

ABSTRACT

BACKGROUND: Brain signal variability (BSV) is an important understudied aspect of brain function linked to cognitive flexibility and adaptive behavior. Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and restricted and repetitive behaviors (RRBs). While atypical brain function has been identified in individuals with ASD using fMRI task-activation and functional connectivity approaches, little is known about age-related relationships with resting-state BSV and repetitive behaviors in ASD.

METHODS: We conducted a cross-sectional examination of resting-state BSV and its relationship with age and RRBs in a cohort of individuals with Autism Brain Imaging Data Exchange (n = 351) and typically developing (TD) individuals (n = 402) aged 5-50 years obtained from the Autism Brain Imaging Data Exchange. RRBs were assessed using the Autism Diagnostic Interview-Revised (ADI-RRB) scale. BSV was quantified using the root-mean-square successive difference (rMSSD) of the resting-state fMRI time series. We examined categorical group differences in rMSSD between ASD and TD groups, controlling for both linear and quadratic age. To identify dimensional relationships between age, group, and rMSSD, we utilized interaction regressors for group x age and group x quadratic age. Within a subset of individuals with ASD (269 subjects), we explored the relationship between rMSSD and ADI-RRB scores, both with and without age considerations. The relationship between rMSSD and ADI-RRB scores was further analyzed while accounting for linear and quadratic age. Additionally, we investigated the relationship between BSV, age, and ADI-RRB scores using interaction regressors for age x RRB and quadratic age x RRB.

RESULTS: When controlling for linear age effects, we observed significant group differences between individuals with ASD and TD individuals in the default-mode network (DMN) and visual network, with decreased BSV in ASD. Similarly, controlling for quadratic age effects revealed significant group differences in the DMN and visual network. In both cases, individuals with ASD showed decreased BSV compared with TD individuals in these brain regions. The group × age interaction demonstrated significant group differences in the DMN, and visual network brain areas, indicating that rMSSD was greater in older individuals compared with younger individuals in the ASD group, while rMSSD was greater in younger individuals compared with older individuals in the TD group. The group × quadratic age interaction showed significant differences in the brain regions included in DMN, with an inverted U-shaped rMSSD-age relationship in ASD (higher rMSSD in younger individuals that slightly increased into middle age before decreasing) and a U-shaped rMSSD-age relationship in TD (higher rMSSD in younger and older individuals compared with middle-aged individuals). When controlling for linear and quadratic age effects, we found a significant positive association between rMSSD and ADI-RRB scores in brain regions within the DMN, salience, and visual network. While no significant results were observed for the linear age × RRB interaction, a significant association between quadratic age and ADI-RRB scores emerged in the DMN, dorsal attention network, and sensorimotor network. Individuals with high ADI-RRB scores exhibited an inverted U-shaped relationship between rMSSD and age, with lower rMSSD levels observed in both younger and older individuals, and higher rMSSD in middle-aged individuals. Those with mid-range ADI-RRB scores displayed a weak inverted U-shaped rMSSD-age association. In contrast, individuals with low ADI-RRB scores showed a U-shaped rMSSD-age association, with higher rMSSD levels in younger and older individuals, but a lower rMSSD in middle-aged individuals.

CONCLUSION: These findings highlight age-related atypical BSV patterns in ASD and their association with repetitive behaviors, contributing to the growing literature on understanding alterations in functional brain maturation in ASD.

PMID:39923093 | PMC:PMC11806755 | DOI:10.1186/s13229-024-00631-3

Mult Scler Relat Disord. 2025 Jan 30;95:106270. doi: 10.1016/j.msard.2025.106270. Online ahead of print.

ABSTRACT

BACKGROUND: Altered brain network function is associated with cognitive impairment in multiple sclerosis (MS), but recent studies highlight a lack of consensus in the field. These differences may relate to the stage of MS, or different definitions for cognitive impairment.

OBJECTIVE: We investigated cognitive impairment and functional connectivity (FC) specifically in SPMS (secondary progressive MS) using resting-state functional MRI (rs-fMRI) and assessed the alterations in FC using two commonly used dichotomous criteria for cognitive impairment.

METHODS: 65 SPMS subjects from a British cohort underwent rs-fMRI at 3T, with independent component analysis of resting state networks. Cognitive impairment, assessed by neuropsychometry, was defined using a z-score of ≤ -1.96SD on ≥ 2 domains (-1.96SD group) or z-score of ≤ -1.5SD on ≥ 2 domains (-1.5SD group).

RESULTS: Cognitive impairment was, as expected, more prevalent in the -1.5SD (47 %) than -1.96SD criteria (30 %) group, despite similar demographics in both; mean age of 55 ± 7.1 years, disease duration 22 ± 9.6 years, median EDSS of 6.0 [range 4.0-6.5]. Adopting the -1.96SD criteria substantially increased the number of altered brain regions, with a 2.8 fold increase in regions showing decreased FC; including the ventral attentional and sensorimotor networks, and 1.5 fold increase in regions showing increased FC; including the precuneus and auditory networks.

CONCLUSIONS: The criteria chosen for cognitive impairment significantly impacts patterns of global FC change and may miss key network alterations, which could impact the efficacy of future therapeutic interventions highlighting the need for a consensus in the field. Agreed cut-offs for designating cognitive impairment could facilitate clinical management including monitoring disease activity, progression, and treatment efficacy.

PMID:39921989 | DOI:10.1016/j.msard.2025.106270

Parkinsonism Relat Disord. 2025 Jan 31;133:107315. doi: 10.1016/j.parkreldis.2025.107315. Online ahead of print.

ABSTRACT

PURPOSE: Craniocervical dystonia (CCD) is a large-scale network disorder that involves functional changes in multiple brain regions. However, the association between these functional changes and the underlying molecular mechanisms has not been explored.

OBJECTIVE: We aimed to characterize the molecular changes associated with the imaging-defined functional architecture of the brain in CCD.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 146 patients with CCD and 137 healthy controls (HCs). Differences in the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were compared between groups. Transcriptomic data were obtained from the Allen Human Brain Atlas to identify the gene expression patterns underlying the affected functional architecture in CCD using partial least squares regression.

RESULTS: Compared to HCs, patients with CCD showed common functional alterations, mainly in the left middle occipital gyrus, right middle occipital gyrus, right calcarine, right precentral gyrus, and left postcentral gyrus. These functional alteration patterns were positively associated with 1763 genes (including five risk genes for dystonia) enriched for synaptic signaling, regulation of trans-synaptic signaling, and neuronal systems, while they were negatively associated with 2318 genes (including eight risk genes for dystonia), which were enriched for monoatomic cation transport, DNA damage response and neurodevelopment.

CONCLUSIONS: Our study reveals a genetic pathological mechanism explaining CCD-related brain functional changes.

PMID:39921933 | DOI:10.1016/j.parkreldis.2025.107315

Brain Struct Funct. 2025 Feb 8;230(2):39. doi: 10.1007/s00429-025-02899-4.

ABSTRACT

The influence of regional brain vasculature on resting-state fMRI BOLD signals is well documented. However, the role of brain vasculature is often overlooked in functional connectivity research. In the present report, utilizing publicly available whole-brain vasculature data in the mouse, we investigate the relationship between functional connectivity and brain vasculature. This is done by assessing interregional variations in vasculature through a novel metric termed vascular similarity. First, we identify features to describe the regional vasculature. Then, we employ multiple linear regression models to predict functional connectivity, incorporating vascular similarity alongside metrics from structural connectivity and spatial topology. Our findings reveal a significant correlation between functional connectivity strength and regional vasculature similarity, especially in anesthetized mice. We also show that multiple linear regression models of functional connectivity using standard predictors are improved by including vascular similarity. We perform this analysis at the cerebrum and whole-brain levels using data from both male and female mice. Our findings regarding the relation between functional connectivity and the underlying vascular anatomy may enhance our understanding of functional connectivity based on fMRI and provide insights into its disruption in neurological disorders.

PMID:39921726 | DOI:10.1007/s00429-025-02899-4

Neurobiol Dis. 2025 Feb 5:106816. doi: 10.1016/j.nbd.2025.106816. Online ahead of print.

ABSTRACT

Spinocerebellar ataxia type 3 (SCA3), a neurodegenerative disorder caused by excess CAG repeats in the ATXN3 gene, leads to progressive cerebellar ataxia and other symptoms. The results of previous studies suggest that trace element dysregulation contributes to neurodegenerative disorder onset. Here, we investigated the relationships of trace element dysregulation with CAG repeat length, clinical severity, and brain structural and functional connectivity in 45 patients with SCA3 and 44 healthy controls (HCs). Blood levels of lithium (Li), selenium (Se), and copper (Cu) were significantly lower in patients with SCA3 than in HCs; Li and Se levels were negatively correlated with CAG repeat length, especially in the manifest subgroup. Diffusion tensor imaging combined with resting-state functional magnetic resonance imaging revealed that Li levels were negatively correlated with fractional anisotropy in the white matter (WM) of bilateral frontal and parietal regions; tractography mapping showed disorder structural connectivity of Li-associated region nerve fiber pathways in patients with SCA3. Dynamic causal modeling analyses showed bidirectional causal connectivity from the inferior parietal lobule(IPL) to the cerebellum was significantly correlated with the blood level of Li in patients with SCA3. Time series correlation-based functional connectivity analysis revealed that the intrinsic connectivities of the bilateral dorsal premotor cortex(PMd) and IPL with local cerebellar regions were significantly weaker in patients with SCA3 than in HCs. Our results suggest that trace element dysregulation, especially Li deficiency, induces brain alterations and clinical manifestations in patients with SCA3; Li supplementation may be beneficial for WM or astrocytes in this patient population.

PMID:39921113 | DOI:10.1016/j.nbd.2025.106816

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Feb 5:111283. doi: 10.1016/j.pnpbp.2025.111283. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) and bipolar disorder (BD) share various clinical behaviors and have confounded clinical diagnoses. Converging studies have suggested MDD and BD as disorders with abnormal communication among functional brain networks involved in mental activity and redirection. However, whether MDD and BD show disease-specific alterations in network information interaction remains unclear. This study collected resting-state functional MRI data of 98 patients with MDD, 55 patients with BD, and sex-, age-, and education-matched 95 healthy controls. Spectral dynamic causal model (spDCM) was used to investigate effective connectivities among three large-scale intrinsic functional networks including the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). Effective connectivities showing disease-specific changes were then used as input features of support vector models to predict clinical symptoms and classify individuals with MDD and BD. Compared with healthy controls, both the MDD and BD groups showed increased DAN → SN connectivity. However, within-network connectivities of DMN and DAN showed opposite effects on the diseases. Notably, MDD and BD also showed different alterations on a connectivity loop of SN → DAN → DMN → SN, which could be used to predict the clinical symptom severity of either MDD or BD. Individuals with MDD and BD could be further classified by using connectivities showing opposite disease effects. Our findings reveal common and unique alterations of network interactions in MDD and BD, and further suggest disease-specific neuroimaging markers for clinical diagnosis.

PMID:39921029 | DOI:10.1016/j.pnpbp.2025.111283

Neuroscience. 2025 Feb 5:S0306-4522(25)00095-8. doi: 10.1016/j.neuroscience.2025.02.011. Online ahead of print.

ABSTRACT

BACKGROUND: This study aims to investigate the changes in the topological organization of WM functional connectivity in individuals with TAO, providing a novel and insightful perspective on the functional disruptions that characterize this condition.

METHODS: This study utilized resting-state functional Magnetic Resonance Imaging (rs-fMRI) to capture blood-oxygen-level-dependent (BOLD) signals and T1-weighted images from patients with TAO and healthy control subjects. Group-level masks for white matter were created to extract WM-related BOLD signals, facilitating the construction of a functional white matter network. Graph theory analysis was subsequently conducted to evaluate global metrics, nodal metrics, and modularity, alongside network-based analysis. Finally, support vector machines (SVM) were employed for classification.

RESULTS: A functional white matter network comprising 128 nodes and their respective connections was identified. The graph theory analysis revealed significant differences primarily in the sigma characteristic of the global small-world metrics, with a notable decrease in betweenness centrality observed in the splenium of the corpus callosum. Modularity analysis indicated significant intra-module variations in modules 03 and 05, while strong inter-module connections were observed between modules 01 and 03, as well as between modules 02 and 04. Furthermore, network-based statistics (NBS) highlighted 13 networks that exhibited significant alterations in the TAO group compared to healthy controls, underscoring the potential impact of TAO on the organization of white matter networks.

CONCLUSION: In our study, we found that patients with TAO exhibited abnormalities in the white matter functional network regarding small-world metrics and modularity, which are related to visual and cognitive functions.

PMID:39921024 | DOI:10.1016/j.neuroscience.2025.02.011

Cogn Neurodyn. 2025 Dec;19(1):36. doi: 10.1007/s11571-024-10209-7. Epub 2025 Feb 4.

ABSTRACT

Poststroke cognitive impairments (PSCI) reflect widespread network dysfunction due to structural damage, abnormal neural activity, or abnormal connections in affected brain regions. The exact influence of these lesioned regions on the related functional network and their role in PSCI remains unclear. We recruited 35 first-time stroke patients who had basal ganglia infarcts and PSCI, along with 29 age-matched healthy controls. We utilized T1-weighted imaging to inspect structural damage with regional gray matter volume (GMV). Resting-state fMRI data were utilized to examine spontaneous activities with regional Wavelet-ALFF metric, investigate dynamic functional connectivity (dFC) by seeding the region with damaged GMV, and further study effective connectivity within the abnormal dFC network and its impact on PSCI. In comparison to HC, patients showed significant reduced GMV in the bilateral Rolandic operculum (ROL), along with notable abnormal Wavelet-ALFF values in the right Precuneus (PCUN) and left Cerebellum_9 (CER9). Particularly, an abnormal dFC network seeded in the left ROL, demonstrating significantly differential between PSCI and HC groups and remaining consistent across all time windows, was observed. This abnormal dFC network comprised the left ROL as the seed region, the right ROL, bilateral PCUN, bilateral CER9, right Superior Temporal Gyrus (STG), and right Parahippocampal Gyrus (PHG). Notably, in patients, impaired functions across various cognitive domains significantly influenced the altered effective connections among the abnormal regions, particularly impacting the connections between structurally damaged regions and those with abnormal spontaneous activity. These findings suggest that altered effective connectivity networks within lesioned regions may contribute to deficits in various cognitive domains in PSCI.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10209-7.

PMID:39917314 | PMC:PMC11794930 | DOI:10.1007/s11571-024-10209-7

Int J Neurosci. 2025 Feb 6:1-15. doi: 10.1080/00207454.2024.2446908. Online ahead of print.

ABSTRACT

Depression is characterized by pervasive cognitive and emotional disturbances, yet the neural mechanisms underlying these deficits remain incompletely understood. This study utilized multimodal neuroimaging, including resting-state functional MRI and structural T1-weighted imaging, alongside the MATRICS Consensus Cognitive Battery (MCCB) and the Hamilton Depression Rating Scale (HAMD), to delineate the structural and functional alterations in the insula in first-episode, medication-naïve patients with depression. Compared to matched healthy controls, patients with depression exhibited significant reductions in gray matter density in the left insula, which were robustly associated with impairments in reasoning and problem-solving abilities. Mediation analyses revealed that insular gray matter density mediated the relationship between depressive symptom severity and cognitive deficits, emphasizing the insula's critical role in linking emotional and cognitive dysfunctions. Furthermore, functional connectivity analyses identified disrupted insula-medial prefrontal cortex circuits, highlighting their contribution to the pathophysiology of depression. These findings underscore the insula's dual role as a structural and functional hub in depression, advancing our understanding of the neural substrates of cognitive dysfunction and informing potential targets for intervention.

PMID:39915081 | DOI:10.1080/00207454.2024.2446908

Front Hum Neurosci. 2025 Jan 22;18:1517565. doi: 10.3389/fnhum.2024.1517565. eCollection 2024.

ABSTRACT

OBJECTIVE: The diagnosis of patients with non-lesional epilepsy (NLE) is relatively challenging because of the absence of a clear focus on imaging, and the underlying pathological mechanism remains unclear. The neuronal activity and functional connectivity of NLE patients are significantly abnormal, and the neuronal activity of epilepsy patients is closely related to cerebral blood flow (CBF). Neurovascular coupling (NVC) offers insights into the relationship between neuronal activity and CBF. Hence, we intend to explore the alterations of NVC in NLE patients and their influences on cognitive function.

METHODS: Clinical data of 24 patients with NLE (15 female; age range 19-40 years; median age 30.5 years) and 39 healthy controls (27 female; age range 19-40 years; median age 30 years) were collected, and resting-state functional magnetic resonance imaging (rs-fMRI) and 3D arterial spin labeling (ASL) were performed. The imaging indexes of amplitude of low-frequency fluctuation (ALFF) and CBF were calculated, respectively, by post-processing analysis. The differences in CBF, ALFF and CBF/ALFF ratio between the two groups were analyzed, along with correlation with clinical data of NLE patients.

RESULTS: Compared with the healthy controls, the CBF of the right parahippocampal gyrus was significantly decreased, and the CBF/ALFF ratio of the right inferior parietal, but supramarginal and angular gyri was significantly increased in NLE patients (p < 0.001). Moreover, the CBF/ALFF ratio was positively correlated with epilepsy depression score (r = 0.546, p = 0.006).

CONCLUSION: NLE patients showed abnormal local NVC, which was associated with the severity of depression. The combined application of rs-fMRI and ASL can comprehensively evaluate the neuronal activity and cerebral blood perfusion in patients with NLE. The abnormal NVC is of great significance for us to explore the central mechanism of the occurrence and development of NLE.

PMID:39911912 | PMC:PMC11794321 | DOI:10.3389/fnhum.2024.1517565

Brain Res Bull. 2025 Feb 3;222:111238. doi: 10.1016/j.brainresbull.2025.111238. Online ahead of print.

ABSTRACT

BACKGROUND: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.

METHODS: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.

RESULTS: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.

CONCLUSIONS: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

PMID:39909352 | DOI:10.1016/j.brainresbull.2025.111238

J Biomed Inform. 2025 Feb 3;163:104784. doi: 10.1016/j.jbi.2025.104784. Online ahead of print.

ABSTRACT

OBJECTIVE: Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barriers to learn multiple Bayesian networks (BNs) from rs-fMRI data.

METHODS: We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates.

RESULTS: The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches.

CONCLUSION: We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.

PMID:39909179 | DOI:10.1016/j.jbi.2025.104784

Psychiatry Res Neuroimaging. 2025 Jan 27;348:111957. doi: 10.1016/j.pscychresns.2025.111957. Online ahead of print.

ABSTRACT

Previous functional magnetic resonance imaging (fMRI) studies showed an abnormal brainstem-to-cortex functional connectivity (FC) in major depressive disorder. However, only few studies analyzed brainstem substructures in treatment-resistant depression (TRD). In this study, we analyzed resting-state seed-based FC between midbrain, pons, medulla oblongata and cortical/subcortical brain regions in patients with TRD (n = 24) and age- and sex-matched healthy controls (n = 24). FC was analyzed in each group and compared between groups. Correlation analyses assessed the relationship between FC strength and depressive symptom severity in regions showing significant group differences in seed-based connectivity. Our findings reveal an increased FC in the midbrain and pons to the precentral gyrus, postcentral gyrus, and temporal gyrus in patients with TRD compared to healthy controls. Interestingly, in TRD patients, FC between midbrain and cortex was negatively correlated with BDI-II scores, indicating a relationship between altered connectivity and self-reported depression severity. It is essential to note that our naturalistic, cross-sectional approach precludes causal conclusions regarding the relationship between FC and pathophysiology of TRD. The small sample size necessitates confirmation in a larger cohort. Midbrain/pons-to-cortex FC was increased in patients with TRD compared to healthy controls. Future studies should explore the relationship between abnormal brainstem-to-cortex FC and depressive symptomatology in more detail.

PMID:39908872 | DOI:10.1016/j.pscychresns.2025.111957

Psychiatry Res. 2025 Jan 26;345:116376. doi: 10.1016/j.psychres.2025.116376. Online ahead of print.

ABSTRACT

Schizophrenia and attention deficit hyperactivity disorder (ADHD) have many contradicting features, but both these disorders share inattention as a core symptom. This study explored how the characteristics of inattention differ between the two disorders. 20 patients with schizophrenia, 20 patients with adult ADHD and 20 healthy controls participated in this study. Comprehensive attention test, Korean Wechsler adult intelligence scale-IV and resting-state functional magnetic resonance imaging (fMRI) were collected, among other things. The schizophrenia and ADHD groups showed low and high levels of functional connectivity in the default mode network (DMN), respectively. Functional connectivity level within the DMN was also positively correlated with processing speed index in the schizophrenia group and positively correlated with the number of divided-attention commission errors in the ADHD group. These results show that schizophrenia and adult ADHD have similarities in the characteristics of attention deficit, in that both may arise from dysregulation within the DMN. However, the differences in the levels of functional connectivity in the DMN between these groups affect how inattention manifests in each group.

PMID:39908657 | DOI:10.1016/j.psychres.2025.116376

Int J MS Care. 2025 Feb 3;27(Q1):34-41. doi: 10.7224/1537-2073.2024-003. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: We sought to examine a potential neural mechanism of reduced depressive symptoms in response to aerobic treadmill walking exercise training in people with multiple sclerosis (MS). This includes a secondary analysis of depressive symptom and MRI data from an original randomized controlled trial (RCT) on exercise effects on learning and memory impairment in people with MS.

METHODS: Ten fully ambulatory people with MS were randomly assigned into either a 12-week aerobic treadmill walking condition or active control condition (ie, stretching and range-of-motion activities). All participants completed the Hospital Anxiety and Depression Scale for measurement of depressive symptoms as well as a resting-state functional MRI (fMRI) before and after the 12-week study period.

RESULTS: There were no between-condition differences in depressive symptoms at baseline. Participants who were randomly assigned to the intervention condition demonstrated reductions in depressive symptoms compared with minimal changes for those who underwent the control condition. This corresponded with significant changes in resting-state functional connectivity within the default-mode network (DMN).

CONCLUSIONS: The overall pattern of results suggests that resting-state functional connectivity within the DMN may represent a potential neural mechanism underlying the beneficial effects of exercise on depressive symptoms in people with MS. Such results warrant future consideration in the design and conduct of future RCTs that aim to treat MS-related depression with aerobic exercise training in people with MS who have been prescreened for clinical depression.

PMID:39906605 | PMC:PMC11791503 | DOI:10.7224/1537-2073.2024-003