Most recent paper
A NIRS-Based Technique for Monitoring Brain Tissue Oxygenation in Stroke Patients
Sensors (Basel). 2024 Dec 21;24(24):8175. doi: 10.3390/s24248175.
ABSTRACT
Stroke is a global health issue caused by reduced blood flow to the brain, which leads to severe motor disabilities. Measuring oxygen levels in the brain tissue is crucial for understanding the severity and evolution of stroke. While CT or fMRI scans are preferred for confirming a stroke due to their high sensitivity, Near-Infrared Spectroscopy (NIRS)-based systems could be an alternative for monitoring stroke evolution. This study explores the potential of fNIRS signals to assess brain tissue in chronic stroke patients along with rehabilitation therapy. To study the feasibility of this proposal, ten healthy subjects and three stroke patients participated. For signal acquisition, two NIRS sensors were placed on the forehead of the subjects, who were asked to remain in a resting state for 5 min, followed by a 30 s motor task for each hand, which consists of opening and closing the hand at a steady pace, with a 1 min rest period in between. Acomplete protocol for placing sensors and a signal processing algorithm are proposed. In healthy subjects, a measurable change in oxygen saturation was found, with statistically significant differences (females p = 0.016, males p = 0.005) between the resting-state and the hand movement conditions. This work showed the feasibility of the complete proposal, including the NIRS sensor, the placement, the tasks protocol, and signal processing, for monitoring the state of the brain tissue cerebral oxygenation in stroke patients undergoing rehabilitation therapy. Thus this is a non-invasive barin assessment test based on fNIRS with the potential to be implemented in non-controlled clinical environments.
PMID:39771909 | DOI:10.3390/s24248175
Multifractal dynamic changes of spontaneous brain activity in psychiatric disorders: Adult attention deficit-hyperactivity disorder, bipolar disorder, and schizophrenia
J Affect Disord. 2025 Jan 5:S0165-0327(25)00008-4. doi: 10.1016/j.jad.2025.01.007. Online ahead of print.
ABSTRACT
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.
PMID:39765289 | DOI:10.1016/j.jad.2025.01.007
Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis
Neuroimage Clin. 2025 Jan 3;45:103731. doi: 10.1016/j.nicl.2025.103731. Online ahead of print.
ABSTRACT
The present study investigated spatial dynamic functional network connectivity (dFNC) in patients with functional hemiparesis (i.e., functional stroke mimics, FSM). The aim of this work was to assess static functional connectivity (large-scale) networks and dynamic brain states, which represent distinct dFNC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 15 patients with FSM (mean age = 42.3 ± 9.4, female = 80 %) and 52 age-matched healthy controls (HC, mean age = 42.1 ± 8.6, female = 73 %). Each patient underwent a resting-state functional MRI scan for spatial dFNC evaluation and transcranial magnetic stimulation protocols for indirect assessment of GABAergic and glutamatergic transmission. We considered three dynamic brain networks, i.e., the somatomotor network (SMN), the default mode network (DMN) and the salience network (SN), each summarized into four distinct recurring spatial configurations. Compared to HC, patients with FSM showed significant decreased dwell time, e.g. the time each individual spends in each spatial state of each network, in state 2 of the SMN (HC vs. FSM, 13.5 ± 27.1 vs. 1.9 ± 4.1, p = 0.044). Conversely, as compared to HC, FSM spent more time in state 1 of the DMN (10.8 ± 14.9 vs. 27.3 ± 38.9, p = 0.037) and in state 3 of the SN (23.1 ± 23.0 vs. 38.8 ± 38.2, p = 0.002). We found a significant correlation between the dwell time of impaired functional state of the SMN and measures of GABAergic neurotransmission (r = 0.581, p = 0.037). Specifically, longer impaired dwell time was associated with greater GABAergic inhibition. These findings demonstrate that FSM present altered functional brain network dynamics, which correlate with measures of GABAergic neurotransmission. Both dFNC and GABAergic neurotransmission may serve as potential targets for future intervention strategies.
PMID:39764901 | DOI:10.1016/j.nicl.2025.103731
Association of Bile Acids with Connectivity of Executive Control and Default Mode Networks in Patients with Major Depression
bioRxiv [Preprint]. 2024 Dec 23:2024.12.20.629637. doi: 10.1101/2024.12.20.629637.
ABSTRACT
OBJECTIVE: Bile acids may contribute to pathophysiologic markers of Alzheimer's disease, including disruptions of the executive control network (ECN) and the default mode network (DMN). Cognitive dysfunction is common in major depressive disorder (MDD), but whether bile acids impact these networks in MDD patients is unknown.
METHODS: Resting state functional magnetic resonance imaging (fMRI) scans and blood measures of four bile acids from 74 treatment-naïve adults with MDD were analyzed. Dorsolateral prefrontal cortex (DLPFC) seeds were used to examine connectivity of the ECN and posterior cingulate cortex (PCC) seeds were used for the DMN. Using a whole-brain analysis, the functional connectivity of these seeds was correlated with serum levels chenodeoxycholic acid (CDCA) and its bacterially-derived secondary bile acid, lithocholic acid (LCA).
RESULTS: CDCA levels were strongly and inversely correlated with connectivity between DLPFC regions of the ECN (R 2 = .401, p<.001). LCA levels were strongly and positively correlated with connectivity of the DLPFC and left inferior temporal cortex of the ECN (R 2 =.263, p<.001). The LCA/CDCA ratio was strongly and positively correlated with connectivity of the DLPFC with two components of the ECN: bilateral inferior temporal cortex and the left superior and inferior parietal lobules (all R 2 >.24, all p<.001). For the DMN, the LCA/CDCA ratio was strongly and negatively correlated with connectivity of the PCC with multiple bilateral insula regions (all R 2 >0.25, all p<.001).
CONCLUSIONS: The relationship between LCA and CDCA levels and functional connectivity of the ECN and DMN suggests potential shared pathophysiologic processes between Alzheimer's disease and MDD.
PMID:39763748 | PMC:PMC11703205 | DOI:10.1101/2024.12.20.629637
Auditory and Visual Thalamocortical Connectivity Alterations in Unmedicated People with Schizophrenia: An Individualized Sensory Thalamic Localization and Resting-State Functional Connectivity Study
medRxiv [Preprint]. 2024 Dec 22:2024.12.18.24319241. doi: 10.1101/2024.12.18.24319241.
ABSTRACT
BACKGROUND: Converging evidence from clinical neuroimaging and animal models has strongly implicated dysfunction of thalamocortical circuits in the pathophysiology of schizophrenia. Preclinical models of genetic risk for schizophrenia have shown reduced synaptic transmission from auditory thalamus to primary auditory cortex, which may represent a correlate of auditory disturbances such as hallucinations. Human neuroimaging studies, however, have found a generalized increase in resting state functional connectivity (RSFC) between whole thalamus and sensorimotor cortex in people with schizophrenia (PSZ). We aimed to more directly translate preclinical findings by specifically localizing auditory and visual thalamic nuclei in unmedicated PSZ and measuring RSFC to primary sensory cortices.
METHODS: In this case-control study, 82 unmedicated PSZ and 55 matched healthy controls (HC) completed RSFC functional magnetic resonance imaging (fMRI). Auditory and visual thalamic nuclei were localized for 55 unmedicated PSZ and 46 HC who additionally completed a sensory thalamic nuclei localizer fMRI task (N = 101). Using localized nuclei as RSFC seeds we assessed group differences in auditory and visual thalamocortical connectivity and associations with positive symptom severity.
RESULTS: Auditory thalamocortical connectivity was not significantly different between PSZ and HC, but hyperconnectivity was associated with greater positive symptom severity in bilateral superior temporal gyrus. Visual thalamocortical connectivity was significantly greater in PSZ relative to HC in secondary and higher-order visual cortex, but not predictive of positive symptom severity.
CONCLUSION: These results indicate that visual thalamocortical hyperconnectivity is a generalized marker of schizophrenia, while hyperconnectivity in auditory thalamocortical circuits relates more specifically to positive symptom severity.
PMID:39763546 | PMC:PMC11702713 | DOI:10.1101/2024.12.18.24319241
Infants' resting-state functional connectivity and event-related potentials: A multimodal approach to investigating the neural basis of infant novelty detection
Dev Psychol. 2025 Jan 6. doi: 10.1037/dev0001892. Online ahead of print.
ABSTRACT
Individual differences in how the brain responds to novelty are present from infancy. A common method of studying novelty processing is through event-related potentials (ERPs). While ERPs possess millisecond precision, spatial resolution remains poor, especially in infancy. This study aimed to balance spatial and temporal precision by combining ERP data with functional magnetic resonance imaging (fMRI) data. Twenty-nine infants (15 female) underwent resting-state fMRI (Mage = 4.73 months) and electroencephalography (EEG) during a three-stimulus auditory oddball task (Mage = 5.19 months). The mismatch response (MMR) and P3 were computed from ERP data, and resting-state functional connectivity (rs-FC) was computed from fMRI data. We first source localized the MMR and P3 responses to five regions-of-interest (ROIs), based on prior literature. We then performed network-level enrichment analyses to identify associations between rs-FC and MMR and P3, at each of the five ROIs. In line with prior work, source-localized EEG analyses implicated the bilateral auditory cortices, posterior cingulate cortex, and superior parietal cortex in the generation of MMR and P3 responses. The MMR and P3 related to functional connectivity within the somatomotor network as well as between the somatomotor and the dorsal and ventral attention networks (DAN/VAN). This was especially true for novelty response ERPs recorded at superior parietal lobule, known for its implications in initial reorienting to novel stimuli. The DAN, known for its implication in initial reorienting to support novelty detection, was implicated for the MMR. In contrast, the VAN, known for its support of later-stage, complex adjustments in attention, related to the later P3. This work further solidifies our understanding of the underlying networks implicated in the development of immediate responses to stimuli. Altered configurations of such networks may increase the risk for heightened sensitivity to novelty in certain individuals, which could have behavioral and clinical significance. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
PMID:39760730 | DOI:10.1037/dev0001892
Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks
Front Neuroinform. 2024 Dec 20;18:1480366. doi: 10.3389/fninf.2024.1480366. eCollection 2024.
ABSTRACT
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.
PMID:39759761 | PMC:PMC11695337 | DOI:10.3389/fninf.2024.1480366
Neuroimaging signatures and a deep learning modeling for early diagnosing and predicting non-pharmacological therapy success for subclinical depression comorbid sleep disorders in college students
Int J Clin Health Psychol. 2024 Oct-Dec;24(4):100526. doi: 10.1016/j.ijchp.2024.100526. Epub 2024 Dec 12.
ABSTRACT
OBJECTIVE: College students with subclinical depression often experience sleep disturbances and are at high risk of developing major depressive disorder without early intervention. Clinical guidelines recommend non-pharmacotherapy as the primary option for subclinical depression with comorbid sleep disorders (sDSDs). However, the neuroimaging mechanisms and therapeutic responses associated with these treatments are poorly understood. Additionally, the lack of an early diagnosis and therapeutic effectiveness prediction model hampers the clinical promotion and acceptance of non-pharmacological interventions for subclinical depression.
METHODS: This study involved pre- and post-treatment resting-state functional Magnetic Resonance Imaging (rs-fMRI) and clinical data from a multicenter, single-blind, randomized clinical trial. The trial included 114 first-episode, drug-naïve university students with subclinical depression and comorbid sleep disorders (sDSDs; Mean age=22.8±2.3 years; 73.7% female) and 93 healthy controls (HCs; Mean age=22.2±1.7 years; 63.4% female). We examined altered functional connectivity (FC) and brain network connective mode related to subregions of Default Mode Network (sub-DMN) using seed-to-voxel analysis before and after six weeks of non-pharmacological antidepressant treatment. Additionally, we developed an individualized diagnosing and therapeutic effect predicting model to realize early recognition of subclinical depression and provide objective suggestions to select non-pharmacological therapy by using the newly proposed Hierarchical Functional Brain Network (HFBN) with advanced deep learning algorithms within the transformer framework.
RESULTS: Neuroimaging responses to non-pharmacologic treatments are characterized by alterations in functional connectivity (FC) and shifts in brain network connectivity patterns, particularly within the sub-DMN. At baseline, significantly increased FC was observed between the sub-DMN and both Executive Control Network (ECN) and Dorsal Attention Network (DAN). Following six weeks of non-pharmacologic intervention, connectivity patterns primarily shifted within the sub-DMN and ECN, with a predominant decrease in FCs. The HFBN model demonstrated superior performance over traditional deep learning models, accurately predicting therapeutic outcomes and diagnosing subclinical depression, achieving cumulative scores of 80.47% for sleep quality prediction and 84.67% for depression prediction, along with an overall diagnostic accuracy of 82.34%.
CONCLUSIONS: Two-scale neuroimaging signatures related to the sub-DMN underlying the antidepressant mechanisms of non-pharmacological treatments for subclinical depression. The HFBN model exhibited supreme capability in early diagnosing and predicting non-pharmacological treatment outcomes for subclinical depression, thereby promoting objective clinical psychological treatment decision-making.
PMID:39759571 | PMC:PMC11699106 | DOI:10.1016/j.ijchp.2024.100526
SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis
Mach Learn Clin Neuroimaging (2024). 2025;15266:46-56. doi: 10.1007/978-3-031-78761-4_5. Epub 2024 Dec 6.
ABSTRACT
Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision. Instead of extracting post-hoc feature attributions to uncover functional connections that are important to the target task, we identify a small subset of highly informative connections during training and occlude the rest. To this end, we jointly train a (1) sparse input mask, (2) variational autoencoder (VAE), and (3) downstream classifier in an end-to-end fashion. While we need a portion of labeled samples to train the classifier, we optimize the sparse mask and VAE with unlabeled data from additional acquisition sites, retaining only the input features that generalize well. We evaluate our method - Sparsely Reconstructed Graphs (SpaRG) - on the public ABIDE dataset for the task of sex classification, training with labeled cases from 18 sites and adapting the model to two additional out-of-distribution sites with a portion of unlabeled samples. For a relatively coarse parcellation (64 regions), SpaRG utilizes only 1% of the original connections while improving the classification accuracy across domains. Our code can be found at www.github.com/yanismiraoui/SpaRG.
PMID:39758707 | PMC:PMC11694515 | DOI:10.1007/978-3-031-78761-4_5
Structure-function coupling in network connectivity and associations with negative affectivity in a group of transdiagnostic adolescents
J Mood Anxiety Disord. 2025 Mar;9:100094. doi: 10.1016/j.xjmad.2024.100094. Epub 2024 Nov 20.
ABSTRACT
The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.
PMID:39758557 | PMC:PMC11694614 | DOI:10.1016/j.xjmad.2024.100094
Higher amplitudes of visual networks are associated with trait- but not state-depression
Psychol Med. 2025 Jan 6:1-12. doi: 10.1017/S0033291724003167. Online ahead of print.
ABSTRACT
Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission ('trait depression group'), those with large longitudinal severity changes in depression symptomatology ('state depression group'), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.
PMID:39757726 | DOI:10.1017/S0033291724003167
Objective outcome prediction in depression through functional MRI brain network dynamics
Psychiatry Res Neuroimaging. 2024 Dec 30;347:111945. doi: 10.1016/j.pscychresns.2024.111945. Online ahead of print.
ABSTRACT
RESEARCH PURPOSE: Subjective clinical decision-making in major depressive disorder (MDD) may result in low treatment effectiveness. This study aims to identify objective predictors of MDD outcome using resting-state functional MRI scans, acquired from 25 MDD patients at baseline. Over a year, patients were assessed every 3 months, labeled as positive or negative outcome (change in depression severity). Group independent component analysis (GICA) identified (sub)networks at different orders, from which static and dynamic (wavelet) fMRI features were extracted. Binary classifiers performed MDD outcome prediction at each follow-up.
PRINCIPAL RESULTS: The total coherence feature, reflecting network interactivity, yielded the highest performance (area under the curve (AUC) of 0.70). In the positive outcome group, total coherence between the default mode network and ventral salience network was increased at all follow-ups. Classification using this feature alone further demonstrated its discriminating capability (AUC of 0.76 ± 0.10 over all follow-ups). These results suggest that a higher switching capability between internal and external brain states predicts symptom improvement. Higher GICA orders, where major networks are divided into subnetworks, yielded optimal classification performance.
MAJOR CONCLUSIONS: Total coherence, a dynamic fMRI measure, achieved the highest classification performance. These findings contribute to the identification of prognostic biomarkers in MDD.
PMID:39756249 | DOI:10.1016/j.pscychresns.2024.111945
Local structural-functional coupling with counterfactual explanations for epilepsy prediction
Neuroimage. 2025 Jan 2;306:120978. doi: 10.1016/j.neuroimage.2024.120978. Online ahead of print.
ABSTRACT
The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.
PMID:39755222 | DOI:10.1016/j.neuroimage.2024.120978
Effects of intermittent theta burst stimulation on cognitive and swallowing function in patients with MCI and dysphagia risk: a randomized controlled trial
BMC Geriatr. 2025 Jan 4;25(1):8. doi: 10.1186/s12877-024-05625-7.
ABSTRACT
BACKGROUND: Mild cognitive impairment (MCI) is a high-risk factor for dementia and dysphagia; therefore, early intervention is vital. The effectiveness of intermittent theta burst stimulation (iTBS) targeting the right dorsal lateral prefrontal cortex (rDLPFC) remains unclear.
METHODS: Thirty-six participants with MCI were randomly allocated to receive real (n = 18) or sham (n = 18) iTBS. Global cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), and executive function was evaluated with the Trail Making Test (TMT), Digital span test (DST) and Stroop color word test (SCWT). Quantitative swallowing measurements were obtained using temporal and kinetic parameters based on the videofluoroscopic swallowing study (VFSS). Resting-state functional magnetic imaging (fMRI) was performed to observe brain plasticity, functional connectivity (FC) values were calculated. All assessments were completed at baseline and two weeks after treatment. Participants received 10 sessions of daily robotic navigated iTBS.
RESULTS: The MoCA score and the SCWT duration of the real group improved significantly compared with that of the sham group. Temporal parameters of VFSS included 5-ml oral transit time (OTT), 5-ml soft palate elevation time (SET) and 10-ml OTT showed a decreasing trend. However, there was significant improvement in 10-ml OTT when choosing patients with OTT exceeding 1000 ms. FC value between the left middle frontal gyrus and the rDLPFC increased significantly in real stimulation group (p < 0.05 with false discovery rate corrected). We found that baseline FC scores were negatively correlated with the SCWT task duration (r = -0.554, p = 0.017) and with the 10-ml OTT (rho = -0.442, p = 0.027) across all participants. Among those in the iTBS group with a pre-10-ml OTT greater than 1000 ms, we observed a positive correlation between changes in MoCA scores and changes in FC values (r = 0.789, p = 0.035). Furthermore, changes in MoCA scores were positively correlated with changes in 10-ml OTT (r = 0.648, p = 0.031), as determined by Pearson analysis.
CONCLUSIONS: Navigated iTBS over the rDLPFC has the potential to improve global cognition, response inhibition ability, and certain aspects of swallowing function for patients with MCI at high risk for dysphagia. Changes in FC between right and left DLPFC may underlie the neural mechanisms responsible for the effectiveness of iTBS targeting the right DLPFC.
PMID:39755599 | DOI:10.1186/s12877-024-05625-7
Altered thalamotemporal structural connectivity is associated with autistic traits in children with ASD
Behav Brain Res. 2025 Jan 2:115414. doi: 10.1016/j.bbr.2024.115414. Online ahead of print.
ABSTRACT
BACKGROUND: Thalamocortical functional and structural connectivity alterations may contribute to clinical phenotype of Autism Spectrum Disorder. As previous studies focused mainly on thalamofrontal connections, we comprehensively investigated between-group differences of thalamic functional networks and white matter pathways projecting also to temporal, parietal, occipital lobes and their associations with core and co-occurring conditions of this population.
METHODS: A total of 38 children (19 with Autism Spectrum Disorder) underwent magnetic resonance imaging and behavioral assessment. Functional and structural scans were processed to analyze between-group thalamic connectivity differences and their relationships to measurements of autistic traits and language abilities.
RESULTS: No functional differences were found between groups across 20 networks in each hemisphere. However, we showed diffusion properties of thalamocortical pathways projecting right and left temporal lobes to be disrupted in children with Autism Spectrum Disorder. Additionally, there was a significant association between diffusion differences of thalamotemporal tracts and severity of autistic traits.
CONCLUSIONS: Our findings on altered thalamotemporal structural but not functional connectivity contribute to the understanding of white matter organization of thalamocortical pathways in children with Autism Spectrum Disorder.
PMID:39755277 | DOI:10.1016/j.bbr.2024.115414
Increased parietal operculum functional connectivity following vestibular rehabilitation in benign paroxysmal positional vertigo patients with residual dizziness: a randomized controlled resting-state fMRI study
Neuroradiology. 2025 Jan 4. doi: 10.1007/s00234-024-03535-4. Online ahead of print.
ABSTRACT
INTRODUCTION: Residual dizziness (RD) is common in patients with benign paroxysmal positional vertigo (BPPV) after successful canalith repositioning procedures. This study aimed to investigate the therapeutic effects of vestibular rehabilitation (VR) on BPPV patients experiencing RD, and to explore the impact of VR on functional connectivity (FC), specifically focusing on the bilateral parietal operculum (OP) cortex.
METHODS: Seventy patients with RD were randomly assigned to either a four-week VR group or a control group that received no treatment. Assessments included the dizziness Visual Analog Scale (VAS), Dizziness Handicap Inventory (DHI), Hamilton Anxiety/Depression Scale (HAMA/HAMD), and resting-state functional magnetic resonance imaging.
RESULTS: The VR group exhibited a significant decline in scores on VAS, DHI, HAMA and HAMD following training (all p < 0.05). Furthermore, the VR group demonstrated increased FC between the left OP and both the left precuneus and left middle frontal gyrus (MFG), and between the right OP and the right MFG (voxel-level p < 0.001; cluster-level p < 0.05, FDR corrected). Additionally, these changes in FC were found to correlate with clinical features, including scores on HAMA (p = 0.012, r = - 0.513) and DHI (p = 0.022, r = - 0.475) after the intervention.
CONCLUSION: This study demonstrated the therapeutic effects of VR in alleviating RD and emotional disorders, as well as in improving overall quality of life. Notably, these positive outcomes might be associated with increased FC between brain regions involved in mood regulation and vestibular processing. Our findings offer novel neuroimaging evidence that supports the hypothesis that VR facilitates dynamic vestibular compensation.
PMID:39754615 | DOI:10.1007/s00234-024-03535-4
Functional connectivity gradients and neurotransmitter maps among patients with mild cognitive impairment and depression symptoms
J Psychiatry Neurosci. 2025 Jan 3;50(1):E11-E20. doi: 10.1503/jpn.240111. Print 2025 Jan-Feb.
ABSTRACT
BACKGROUND: Both depressive symptoms and neurotransmitter changes affect the characteristics of functional brain networks in clinical patients. We sought to explore how brain functional grading is organized among patients with mild cognitive impairment and depressive symptoms (D-MCI) and whether changes in brain organization are related to neurotransmitter distribution.
METHODS: Using 3 T magnetic resonance imaging (MRI) we acquired functional MRI (fMRI) data from patients with D-MCI, patients with mild cognitive impairment without depression (nD-MCI), and healthy controls. We used resting-state fMRI and diffusion embedding to examine the pattern of functional connectivity gradients. We used analysis of covariance and post hoc t tests to compare the difference in functional connectivity gradients among the 3 groups. We examined the correlation between variations in functional connectivity gradients and neurotransmitter maps using the JuSpace toolbox.
RESULTS: We included 105 participants, including 31 patients with D-MCI, 40 patients with nD-MCI, and 34 healthy controls. Compared with healthy controls, both the nD-MCI and D-MCI groups showed abnormalities in the principal unimodal-transmodal gradient pattern. Compared with controls, the D-MCI group showed an increased secondary gradient in the default mode network. Differences in the functional connectivity gradients between the D-MCI and nD-MCI groups were significantly correlated with the distribution of 5-hydroxytryptamine receptor subtype 1A.
LIMITATIONS: The small sample size affects the generalizability of the results, and the neurotransmitter template is based on healthy participants, not patients.
CONCLUSION: Our results suggest that depressive symptoms cause abnormalities in the hierarchical segregation of functional brain organization among patients with MCI. Such abnormal changes may be related to the distribution of neurotransmitters.
PMID:39753307 | DOI:10.1503/jpn.240111
Functional connectivity within sensorimotor cortical and striatal regions is regulated by sepsis in a sex-dependent manner
Neuroimage. 2025 Jan 1:120995. doi: 10.1016/j.neuroimage.2024.120995. Online ahead of print.
ABSTRACT
Sepsis is a state of systemic immune dysregulation and organ failure that is frequently associated with severe brain disability. Epidemiological studies have indicated that younger females have better prognosis and clinical outcomes relative to males, though the sex-dependent response of the brain to sepsis during post-sepsis recovery remains largely uncharacterized. Using a modified polymicrobial intra-abdominal murine model of surgical sepsis, we characterized the acute effects of intra-abdominal sepsis on peripheral inflammation, brain inflammation and brain functional connectivity in young adult mice of both sexes. Following sepsis, both male and female mice survived the procedure, regained body weight within 7 days post-sepsis and showed reduced diversity in their gut microbiome. Interestingly, compared to the sepsis-induced changes observed in female mice, the post-septic male mice exhibited a comparatively robust profile of splenic cell expansion and intracerebral glial proliferation relative to their healthy counterparts. Analysis of resting-state functional Magnetic Resonance Imaging (fMRI) data collected from the post-septic mice revealed that while connectivity to the somatosensory cortex were affected equally in both sexes, intra-network connectivity strength in the striatum preferentially increased in post-septic males but remained near baseline in post-septic female mice. Additionally, the female mice showed reduced network connectivity alterations in the projections from periaqueductal gray to the superior colliculus as also between the anterior cingulate cortex and the striatum. Coupled with the sustained intracerebral gliosis response, the intra-striatal fMRI response patterns in males could signify a delayed recovery from sepsis. Together, our study provides evidence that peripheral sepsis influences peripheral immunity, brain immunity and brain connectivity in a sex-dependent manner, with the fMRI response strongly indicating cognitive benefits in young females recovering from sepsis relative to their male counterparts.
PMID:39753162 | DOI:10.1016/j.neuroimage.2024.120995
Cardiorespiratory Dynamics in the Brain: Review on the Significance of Cardiovascular and Respiratory Correlates in functional MRI signal
Neuroimage. 2025 Jan 1:121000. doi: 10.1016/j.neuroimage.2024.121000. Online ahead of print.
ABSTRACT
Cardiorespiratory signals have long been treated as "noise" in functional magnetic resonance imaging (fMRI) research, with the goal of minimizing their impact to isolate neural activity. However, there is a growing recognition that these signals, once seen as confounding variables, provide valuable insights into brain function and overall health. This shift reflects the dynamic interaction between the cardiovascular, respiratory, and neural systems, which together support brain activity. In this review, we explore the role of cardiorespiratory dynamics-such as heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and changes in blood flow, oxygenation, and carbon dioxide levels-embedded within fMRI signals. These physiological signals reflect critical aspects of neurovascular coupling and are influenced by factors such as physiological stress, breathing patterns, and age-related changes. We also discuss the complexities of distinguishing these signals from neuronal activity in fMRI data, given their significant contribution to signal variability and interactions with cerebrospinal fluid (CSF). Recognizing the influence of these cardiorespiratory dynamics is crucial for improving the interpretation of fMRI data, shedding light on heart-brain and respiratory-brain connections, and enhancing our understanding of circulation, oxygen delivery, and waste elimination within the brain.
PMID:39753161 | DOI:10.1016/j.neuroimage.2024.121000
Classification of Irritable Bowel Syndrome Using Brain Functional Connectivity Strength and Machine Learning
Neurogastroenterol Motil. 2025 Jan 3:e14994. doi: 10.1111/nmo.14994. Online ahead of print.
ABSTRACT
BACKGROUND: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diagnostic criteria and biomarkers. This study aims to investigate brain network alterations in IBS patients using the functional connectivity strength (FCS) method and to develop a support vector machine (SVM) classifier for distinguishing IBS patients from healthy controls (HCs).
METHODS: Thirty-one patients with IBS and thirty age and sex-matched HCs were enrolled in this study and underwent resting-state functional magnetic resonance imaging (fMRI) scans. We applied FCS to assess global brain functional connectivity changes in IBS patients. An SVM-based machine - learning approach was then used to evaluate whether the altered FCS regions could serve as fMRI-based markers for classifying IBS patients and HCs.
RESULTS: Compared to the HCs, patients with IBS showed significantly increased FCS in the left medial orbitofrontal cortex (mOFC) and decreased FCS in the bilateral cingulate cortex/precuneus (PCC/Pcu) and middle cingulate cortex (MCC). The machine-learning model achieved a classification accuracy of 91.9% in differentiating IBS patients from HCs.
CONCLUSION: These findings reveal a unique pattern of FCS alterations in brain areas governing pain regulation and emotional processing in IBS patients. The identified abnormal FCS features have the potential to serve as effective biomarkers for IBS classification. This study may contribute to a deeper understanding of the neural mechanisms of IBS and aid in its diagnosis in clinical practice.
PMID:39752374 | DOI:10.1111/nmo.14994