Most recent paper

Mol Psychiatry. 2025 Feb 21. doi: 10.1038/s41380-025-02897-2. Online ahead of print.

ABSTRACT

Chronic stress is a causal antecedent condition for major depressive disorder and associates with altered patterns of neural connectivity. There are nevertheless important individual differences in susceptibility to chronic stress. How functional connectivity (FC) amongst interconnected, depression-related brain regions associates with resilience and susceptibility to chronic stress is largely unknown. We used resting-state functional magnetic resonance imaging (rs-fMRI) to examine FC between established depression-related regions in susceptible (SUS) and resilient (RES) adult mice following chronic social defeat stress (CSDS). Seed-seed FC analysis revealed that the ventral dentate gyrus (vDG) exhibited the greatest number of FC group differences with other stress-related limbic brain regions. SUS mice showed greater FC between the vDG and subcortical regions compared to both control (CON) or RES groups. Whole brain vDG seed-voxel analysis supported seed-seed findings in SUS mice but also indicated significantly decreased FC between the vDG and anterior cingulate area compared to CON mice. Interestingly, RES mice exhibited enhanced FC between the vDG and anterior cingulate area compared to SUS mice. Moreover, RES mice showed greater FC between the infralimbic prefrontal cortex and the nucleus accumbens shell compared to CON mice. These findings indicate unique differences in FC patterns in phenotypically distinct SUS and RES mice that could represent a neurobiological basis for depression, anxiety, and negative-coping behaviors that are associated with exposure to chronic stress.

PMID:39984680 | DOI:10.1038/s41380-025-02897-2

J Affect Disord. 2025 Feb 19:S0165-0327(25)00261-7. doi: 10.1016/j.jad.2025.02.054. Online ahead of print.

ABSTRACT

OBJECTIVE: The "ideation-to-action" framework for suicide posited that the occurrence of suicidal ideation (SI) and suicide attempts (SA) may involve distinct neural mechanisms. However, the disparities in neurocognitive impairment between SI and SA, along with the underlying neural mechanisms, remain further investigation.

METHODS: A total of 730 participants were recruited, including depressed patients with recent SA (RSA group, n = 69), patients with recent SI but no history of SA (SI group, n = 327), patients without SI or SA (NSI group, n = 87) and 247 healthy controls. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans and completed cognitive function assessments. The dynamic regional homogeneity (dReHo) was measured using a sliding-window approach. Differences in dReHo and cognitive function between groups were analyzed.

RESULTS: The SI group demonstrated decreased verbal memory compared to the NSI group. Critically, the RSA group exhibited more pronounced verbal memory impairment than the SI group, accompanied by deficits in working memory and cognitive flexibility. Neuroimaging results revealed elevated dReHo values in the right precuneus (PCUN.R) in the RSA group, distinguishing them from the SI, NSI, and HC groups. Additionally, the increased dReHo of PCUN.R was correlated with diminished cognitive performance.

CONCLUSIONS: Depressed patients with SA showed elevated variability of functional activity in PCUN.R, which may be related to their more severe cognitive impairment. This may be a potential neural basis for the development of SA. Our findings offer new insights for advancing neuroscience research on the progression from SI to SA.

PMID:39983773 | DOI:10.1016/j.jad.2025.02.054

Neuroimage Clin. 2025 Feb 11;45:103753. doi: 10.1016/j.nicl.2025.103753. Online ahead of print.

ABSTRACT

Resting state functional MRI (rsfMRI) exploits variations in blood-oxygenation-level-dependent (BOLD) signals to infer resting state functional connectivity (FC) within and between brain networks. However, there have been few reports quantifying and validating the results of rsfMRI analyses with other metrics of brain circuits. We measured longitudinal changes in FC both within and between brain networks in three squirrel monkeys after focal lesions of the thalamic ventroposterior lateral nucleus (VPL) that were intended to disrupt the input to somatosensory cortex and impair manual dexterity. Local field potential signals were recorded to assess electrophysiological changes during each animal's recovery, and behavioral performances were measured longitudinally using a sugar-pellet grasping task. Finally, end-point histological evaluations were performed on brain tissue slices to quantify the VPL damage. The rsfMRI data analysis showed significant decrease in FC measures both within and between networks immediately post-injury, which started to recover at different time-points for each animal. The trajectories of FC recovery for each animal mirrored their individual behavioral recovery time-courses. Electrophysiological measurements of inter-electrode coherences and end-point histological measures also aligned well with the graded injury effects measured using rsfMRI-based FC. A simple algorithm employing FC measures from the somatosensory network could accurately predict each monkeys' behavioral recovery timeframe after four weeks post-injury. Whole brain between-network FC measures further revealed that the injury effects were not limited to thalamocortical connections but were rather more widespread. Overall, this study provides evidence of the validity of rsfMRI based FC measures as indicators of the functional integrity and behavioral relevance following an injury to a specific brain circuit.

PMID:39983550 | DOI:10.1016/j.nicl.2025.103753

Psychiatry Res Neuroimaging. 2025 Feb 11;348:111961. doi: 10.1016/j.pscychresns.2025.111961. Online ahead of print.

ABSTRACT

BACKGROUND: Depression is linked to abnormalities in brain networks. Resting-state functional connectivity (FC), as measured using resting-state fMRI (rs-fMRI), is a crucial tool for exploring the brain network abnormalities associated with depressive symptoms, as it reveals how disruptions in brain region interactions occur. However, research focusing on adolescents with depression is limited and inconsistent, highlighting the need for further studies in this area.

METHODS: Fifty-five adolescents with Depressive episodes (DE) and 26 healthy controls (HCs) underwent resting-state fMRI. Depressive symptoms were assessed using the 17-item Hamilton Rating Scale for Depression (HAMD-17). Seed regions were defined based on Yeo's seven-network scheme, including the sensorimotor network (SMN), ventral attention network (VAN), dorsal attention network (DAN), visual network (VN), frontoparietal network (FPN), default mode network (DMN), and limbic network (LN). These seed regions were derived from analysis of large-scale FC in healthy individuals, and were selected for their relevance to cognition, emotion, and depression research. Network-based statistical analyses were used to compare the adolescents with DE to the HCs, and correlation analyses were employed to examine the relationships between FC changes and cognitive performance.

RESULTS: The results showed significant differences in FC between the DE and HCs groups, involving 17 nodes and 17 edges across seven networks. Decreased FC was observed within the FPN, as well as between the FPN and VAN, the FPN and DMN, and the SMN and both the DAN and VN. Increased FC was observed between the FPN and VN, between the DAN and other networks (i.e., the DMN and FPN), and between the SMN and multiple networks. Notably, FC between the right superior parietal (SMN) and right precuneus (DMN) showed a negative correlation with HAMD-17 scores.

CONCLUSION: These results suggest that adolescents with DE experience widespread brain network abnormalities characterized by hypoactivity in external networks such as the SMN and VN, as well as hyperactivity in associative regions, including the DMN, FPN, SMN, and LN. Although these changes in FC are evident, the specific mechanisms linking them to clinical symptoms remain unclear and warrant further investigation.

PMID:39983531 | DOI:10.1016/j.pscychresns.2025.111961

PLoS One. 2025 Feb 21;20(2):e0317566. doi: 10.1371/journal.pone.0317566. eCollection 2025.

ABSTRACT

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

PMID:39982930 | DOI:10.1371/journal.pone.0317566

Bipolar Disord. 2025 Feb 21. doi: 10.1111/bdi.70013. Online ahead of print.

ABSTRACT

INTRODUCTION: Exposure to childhood maltreatment can contribute to multiple behavioral and clinical manifestations, including the development of psychotic illnesses and pain-related abnormalities. Aberrant pain perception in individuals with psychosis may be associated with the worsening psychiatric symptoms, including an increase in mood episodes and a higher risk for suicidality. Despite the multiple connections between psychosis, pain, and childhood maltreatment, the combined investigation of these three domains remains limited.

METHODS: In this study, patients with schizophrenia (SZ, n = 20) or bipolar I disorder (BD, n = 24) and healthy controls (HC, n = 24) underwent a comprehensive clinical evaluation followed by quantitative sensory testing (QST), where behavioral sensitivity to thermal stimuli was quantified. Central pain circuitry was probed using a combination of functional and structural magnetic resonance imaging. Neuroimaging analyses focused on thermal stimulation fMRI responses, resting-state connectivity, and gray matter morphological properties.

RESULTS: fMRI demonstrated diminished sensorimotor activation during an evoked pain state for both SZ and BD patients, where reduced activity in thalamic subdivisions (i.e., pulvinar nucleus) in BD patients negatively correlates with the severity of childhood maltreatment. Resting-state connectivity analyses revealed altered connectivity of various cortical regions with the postcentral gyri and thalamic nuclei, suggesting potential altered neural mechanisms underlying pain perception in patients with SZ and BD. Morphological analysis identified reduced gray matter thickness in the postcentral sulcus of BD patients, which correlated with the severity of childhood maltreatment.

CONCLUSION: These findings provide insight into the multidimensional nature of clinical presentations in SZ and BD and contribute to our understanding of the complex relationship between childhood maltreatment and central pain processing in patients with psychotic illnesses.

PMID:39981600 | DOI:10.1111/bdi.70013

Hum Brain Mapp. 2025 Feb 15;46(3):e70049. doi: 10.1002/hbm.70049.

ABSTRACT

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

PMID:39980439 | DOI:10.1002/hbm.70049

Neuroimage. 2025 Feb 18:121096. doi: 10.1016/j.neuroimage.2025.121096. Online ahead of print.

ABSTRACT

Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

PMID:39978705 | DOI:10.1016/j.neuroimage.2025.121096

J Neurosci Methods. 2025 Feb 18:110406. doi: 10.1016/j.jneumeth.2025.110406. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).

NEW METHOD: We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.

RESULTS: Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r=0.81, p<0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.

COMPARISON WITH EXISTING METHODS: Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.

CONCLUSIONS: Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.

PMID:39978480 | DOI:10.1016/j.jneumeth.2025.110406

J Affect Disord. 2025 Feb 18:S0165-0327(25)00236-8. doi: 10.1016/j.jad.2025.02.033. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks.

METHODS: To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes.

RESULTS: Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations.

CONCLUSIONS: Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.

PMID:39978475 | DOI:10.1016/j.jad.2025.02.033

Neurology. 2024 Apr 9;102(7_supplement_1):3357. doi: 10.1212/WNL.0000000000205084. Epub 2024 Apr 9.

ABSTRACT

OBJECTIVE: To investigate the impact of tribromoethanol, isoflurane, and ketamine/xylazine on neuronally- and hemodynamically-based functional connectivity.

BACKGROUND: Resting-state functional connectivity (RSFC) captures correlated signals among brain regions while at rest. In humans, RSFC is imaged using fMRI by tracking spontaneous blood-oxygen-level-dependent (BOLD) fluctuations. Although distinct anesthetics have been shown to modulate RSFC in mice via a BOLD-like hemodynamic signal, the exploration of their effects on the neuronally based signals is less well known.

DESIGN/METHODS: We used Thy1-GCaMP6f mice with a genetically encoded calcium indicator in excitatory pyramidal neurons, to detect neuronal calcium activity. We implanted a chronic imaging window, followed by GCaMP fluorescence and optical intrinsic signal imaging. Each mouse sequentially received each of the anesthetics-tribromoethanol, isoflurane, or ketamine/xylazine-in random order. We calculated several connectivity metrics including a bihemispheric connectivity index (BCI) to determine the overall connectivity between homotopic regions on each hemisphere. Correlation coefficients were z-transformed to enable comparisons between groups.

RESULTS: Tribromoethanol consistently exhibited the highest BCI values. Tribromoethanol's z-transformed BCI for neuronal GCaMP and hemodynamic connectivity was significantly higher than the metrics for ketamine/xylazine and isoflurane (tribromoethanol 1.06, ketamine/xylazine 0.67, isoflurane 0.80, p < 0.01 for tribromoethanol vs. others). Ketamine/xylazine displayed reduced variability when compared to tribromoethanol and isoflurane. All anesthetics had high correlations between the GCaMP signal and the oxy-hemoglobin signal, with ketamine/xylazine displaying the highest z-transformed correlation of the group (ketamine/xylazine 1.33, tribromoethanol 1.09, isoflurane 1.01), p < 0.01 for KX vs. others).

CONCLUSIONS: While all three anesthetics demonstrate varied effects, tribromoethanol notably optimized BCI compared to ketamine/xylazine and iso. Isoflurane displayed marked variability. This study underscores the importance of anesthetic selection for studies involving functional connectivity. Disclosure: Mr. Lai has nothing to disclose. Tao Qin has received personal compensation for serving as an employee of Helix Nanotechnologies. Prof. Boas has received personal compensation for serving as an employee of Boston University. An immediate family member of Prof. Boas has received personal compensation for serving as an employee of Massachusetts General Hospital. The institution of Prof. Boas has received research support from NIH. The institution of Dr. Sakadzic has received research support from National Institute of Health. Dr. Ayata has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Quris. Dr. Ayata has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Neurelis. The institution of Dr. Ayata has received research support from NIH. The institution of Dr. Ayata has received research support from Takeda. The institution of Dr. Ayata has received research support from Neurelis. Dr. Chung has received research support from NIH/NINDS. Dr. Chung has received research support from The Aneurysm and AVM Foundation.

PMID:39977922 | DOI:10.1212/WNL.0000000000205084

Neurology. 2024 Apr 9;102(7_supplement_1):6310. doi: 10.1212/WNL.0000000000206385. Epub 2024 Apr 9.

ABSTRACT

OBJECTIVE: To determine the feasibility and reliability of using precision-mapping techniques for people with Parkinson disease.

BACKGROUND: Standard resting-state functional connectivity (RSFC) approaches collect small amounts of data, typically ≤ 10 min, and rely on group-average network definitions. An innovative new approach applies precision-mapping techniques, with > 40min of data, to identify individual-level RSFC network maps. Precision-mapping RSFC reveals individual differences in network size, strength, and location.

DESIGN/METHODS: We tested the feasibility and reliability of precision-mapping RSFC for people with Parkinson disease. Participants completed multiple fMRI sessions (3-5) up to seven months apart. Using stringent motion censoring, we determined the amount of low-motion, high quality fMRI data per person to establish feasibility. We compared the similarity of RSFC maps across sessions to examine stability and applied split-half analyses to measure the reliability of RSFC maps based on amount fMRI data.

RESULTS: Preliminary analyses reveal the high feasibility and strong reliability of precision-mapping RSFC for people with Parkinson disease. All participants completed multiple fMRI sessions with large amounts of low motion data for each person (>40 min per person, frame retention average = 75%). Individual participant RSFC maps were stable across sessions (r > 0.7) and highly reliable with >40min of data (split-half reliability, r > 0.8).

CONCLUSIONS: These results demonstrate the feasibility and reliability of using the precision-mapping technique for identifying individual-level RSFC networks in Parkinson disease. With this approach, it will now be possible to examine how individual-level variability of RSFC networks relates to variability in clinical manifestations and predicts progression of Parkinson disease. Disclosure: The institution of Meghan C. Campbell has received research support from NIH. The institution of Meghan C. Campbell has received research support from NIH. The institution of Meghan C. Campbell has received research support from McDonnell Center for Systems Neuroscience. The institution of Meghan C. Campbell has received research support from WUSM Radiology Department. The institution of Meghan C. Campbell has received research support from NIH. Meghan C. Campbell has received personal compensation in the range of $0-$499 for serving as a Grant Reviewer with Parkinson Foundation. Meghan C. Campbell has received personal compensation in the range of $500-$4,999 for serving as a Grant Reviewer with Department of Defense. Ms. Grossen has nothing to disclose. Ms. Carr has nothing to disclose. Dr. Eid has nothing to disclose. The institution of Dr. Norris has received research support from NIH, DMRF, Dysphonia International. Ms. Dworetsky has nothing to disclose. Prof. Gratton has nothing to disclose.

PMID:39977890 | DOI:10.1212/WNL.0000000000206385

Epilepsia Open. 2025 Feb 20. doi: 10.1002/epi4.70002. Online ahead of print.

ABSTRACT

OBJECTIVE: Altered intrinsic brain networks have been revealed in patients with epilepsy and are strongly associated with network reorganization in the latent period. However, the development and reliability of intrinsic brain networks in the early period of epileptogenesis are not well understood. The current study aims to fill this gap by investigating the test-retest reliability of intrinsic brain networks in the early stage of epileptogenesis.

METHODS: We used the rat intrahippocampal kainic acid model of mesial temporal lobe epilepsy. Three sessions of resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired over a 2-week period from 9 sham control rats and 12 rats that later developed spontaneous epilepsy (KA). A group independent component analysis (GICA) approach was used to identify the intrinsic brain networks. Both within and between networks were identified, and test-retest reliability was assessed using the intraclass correlation coefficient (ICC).

RESULTS: Our results showed good-to-excellent within-network stability of resting-state functional brain connectivity in most intrinsic brain networks in sham control rats and in the KA group, except for frontal cortex (FCN) and hippocampal networks (HPN). Further analysis of the between networks showed an increase in variation in the KA brain compared to the sham controls.

SIGNIFICANCE: Overall, our study demonstrated a "moderately stable" phase of the intrinsic brain network in a 2-week latent period window, with an altered between- and within-network connectome feature.

PLAIN LANGUAGE SUMMARY: This fMRI study explored how brain connectivity changes in healthy animals compared to animals in the latent period of epilepsy. We found that functional connectivity increased during the latent period compared to the control group, and this increase persisted across all tested sessions. Additionally, brain networks became less stable in the epilepsy group, particularly in the frontal cortex and hippocampus. These observations provide further insight into how brain networks change and persist during the early stages of epileptogenesis.

PMID:39976075 | DOI:10.1002/epi4.70002

Neuroreport. 2025 Feb 4. doi: 10.1097/WNR.0000000000002139. Online ahead of print.

ABSTRACT

To date, most studies on autism spectrum disorder (ASD) have focused on specific age ranges, while the mechanisms underlying the entire developmental process of autism patients remain unclear. The aim of this study was to investigate the alterations in brain function in autistic individuals at different developmental stages by resting-state functional MRI (rs-fMRI). We obtained rs-fMRI data from 173 ASD and 178 typical development (TD) individuals in Autism Brain Imaging Data Exchange, spanning child, adolescent, and adult groups. We characterized local brain activity using the amplitude of low-frequency fluctuations (ALFFs), regional homogeneity (ReHo), dynamic ALFF (dALFF), and dynamic ReHo (dReHo) metrics. Pearson correlation analyses were conducted on relationships between Autism Diagnostic Observation Schedule scores and activity measures in abnormal brain regions. We found abnormal ALFF values in the medial and lateral orbitofrontal gyrus and right insula cortex with ASD compared with the TD group. In addition, compared with adolescents with ASD, we found that adults with ASD exhibited an increase in dReHo values in the posterior lateral frontal lobe. We also found that changes in ALFF were associated with the severity of autism. We found abnormal activity in multiple brain regions in individuals with autism and correlated it with clinical characteristics. Our results may provide some help for further exploring the age-related neurobiological mechanisms of ASD patients.

PMID:39976045 | DOI:10.1097/WNR.0000000000002139

ArXiv [Preprint]. 2025 Jan 27:arXiv:2501.16409v1.

ABSTRACT

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities, and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding window strategy. Then, we simultaneously employ a temporal block and a spatial block to capture higher-order representations of dynamic spatio-temporal dependencies, via mapping them into an efficient fused feature representation. To further enhance the robustness of these feature representations by reducing the dependency on labeled data, we also introduce a contrastive learning strategy to manipulate different brain states. Experimental results on 345 subjects with 570 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the superiority of our proposed method for MCI (Mild Cognitive Impairment, the prodromal stage of AD) prediction, highlighting its potential for early identification of AD.

PMID:39975430 | PMC:PMC11838685

bioRxiv [Preprint]. 2025 Feb 2:2025.01.29.635539. doi: 10.1101/2025.01.29.635539.

ABSTRACT

Resting-state functional MRI (rs-fMRI) provides valuable insights into brain function during rest, but faces challenges in clinical applications due to individual differences in functional connectivity. While Independent Component Analysis (ICA) is commonly used, it struggles to balance individual variations with inter-subject information. To address this, constrained ICA (cICA) approaches have been developed using templates from multiple datasets to improve accuracy and comparability. In this study, we collected rs-fMRI data from 100,517 individuals across diverse datasets. Data were preprocessed through a standard fMRI pipeline. Our method first used replicable fMRI component templates as priors in constrained ICA (the NeuroMark pipeline), then estimated dynamic functional network connectivity (dFNC). Through clustering analysis, we generated replicable dFNC states, which were then used as priors in constrained ICA to automatically estimate subject-specific states from new subjects.This approach provides a robust framework for analyzing individual rs-fMRI data while maintaining consistency across large datasets, potentially advancing clinical applications of rs-fMRI.

PMID:39975182 | PMC:PMC11838263 | DOI:10.1101/2025.01.29.635539

bioRxiv [Preprint]. 2025 Jan 27:2025.01.25.634845. doi: 10.1101/2025.01.25.634845.

ABSTRACT

INTRODUCTION: Numerous studies have emphasized the time-varying modular architecture of functional brain networks and its relevance to cognitive functions in healthy participants. However, how brain modular dynamics change in schizophrenia and how these alterations relate to neurotransmitter and transcriptomic signatures have not been well elucidated.

METHODS: We harmonized resting-state fMRI data from a multi-site sample including 223 patients and 279 healthy controls and applied the multilayer network method to estimate the regional module switching rate (flexibility) of functional brain connectomes. We examined aberrant flexibility in patients relative to controls and explored its relations to neurotransmitter systems and postmortem gene expression.

RESULTS: Compared with controls, patients with schizophrenia had significantly higher flexibility in the somatomotor and right visual regions, and lower flexibility in the left parahippocampal gyrus, right supramarginal gyrus, right frontal-operculum-insula, bilateral precuneus posterior cingulate cortex, and bilateral inferior parietal gyrus. These alterations were associated with multiple neurotransmitter systems and weighted gene transcriptomic profiles. The most relevant genes were preferentially enriched for biological processes of transmembrane transport and brain development, specific cell types, and previously identified schizophrenia-related genes.

CONCLUSIONS: This study reveals aberrant modular dynamics in schizophrenia and its relations to neurotransmitter systems and schizophrenia-related transcriptomic profiles, providing insights into the understanding of the pathophysiology underlying schizophrenia.

PMID:39974915 | PMC:PMC11838238 | DOI:10.1101/2025.01.25.634845

World J Psychiatry. 2025 Feb 19;15(2):102412. doi: 10.5498/wjp.v15.i2.102412. eCollection 2025 Feb 19.

ABSTRACT

BACKGROUND: Currently, adolescent depression is one of the most significant public health concerns, markedly influencing emotional, cognitive, and social maturation. Despite advancements in distinguish the neurobiological substrates underlying depression, the intricate patterns of disrupted brain network connectivity in adolescents warrant further exploration.

AIM: To elucidate the neural correlates of adolescent depression by examining brain network connectivity using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: The study cohort comprised 74 depressed adolescents and 59 healthy controls aged 12 to 17 years. Participants underwent rs-fMRI to evaluate functional connectivity within and across critical brain networks, including the visual, default mode network (DMN), dorsal attention, salience, somatomotor, and frontoparietal control networks.

RESULTS: Analyses revealed pronounced functional disparities within key neural circuits among adolescents with depression. The results demonstrated existence of hemispheric asymmetries characterized by enhanced activity in the left visual network, which contrasted the diminished activity in the right hemisphere. The DMN facilitated increased activity within the left prefrontal cortex and reduced engagement in the right hemisphere, implicating disrupted self-referential and emotional processing mechanisms. Additionally, an overactive right dorsal attention network and a hypoactive salience network were identified, underscoring significant abnormalities in attentional and emotional regulation in adolescent depression.

CONCLUSION: The findings from this study underscore distinct neural connectivity disruptions in adolescent depression, underscoring the critical role of specific neurobiological markers for precise early diagnosis of adolescent depression. The observed functional asymmetries and network-specific deviations elucidate the complex neurobiological architecture of adolescent depression, supporting the development of targeted therapeutic strategies.

PMID:39974491 | PMC:PMC11758046 | DOI:10.5498/wjp.v15.i2.102412

J Neuromuscul Dis. 2025 Jan-Feb;12(1):22143602241307197. doi: 10.1177/22143602241307197.

ABSTRACT

INTRODUCTION: Myotonic dystrophy type 1 (DM1) patients show both structural and functional brain alterations, including abnormal resting-state (RS) functional connectivity. Although some studies have investigated RS functional connectivity in DM1, methodological differences make it challenging to draw consistent conclusions.

OBJECTIVES: This study aims to analyze 1) RS functional connectivity in DM1 patients compared to healthy controls (HC), 2) graph theory metrics, 3) longitudinal connectivity variations, and 4) the relationship between connectivity and clinical, cognitive, and structural brain data.

METHODOLOGY: Twenty-one DM1 patients and 21 matched HCs underwent 3 T MRI scans, including RS fMRI. Of these, 15 DM1 patients and 13 HCs participated in the follow-up after 3 years. Additionally, DM1 patients underwent baseline clinical, molecular and cognitive assessments. Functional connectivity analysis (ROI-to-ROI) and graph theory measures were employed. Longitudinal changes in connectivity were examined, and total hyperconnectivity and hypoconnectivity values were calculated to explore correlations with clinical, brain, and cognitive correlates.

RESULTS: DM1 patients showed widespread hyperconnectivity compared to HCs. Although no statistically significant differences were found in graph theory measures, patients tended to show decreased efficiency, strength, and clustering (with moderate effect sizes). Patients remained hyperconnected over time, with a progression similar to HCs. Hyperconnectivity was associated with more severe disease, greater muscular impairment, and molecular defects, as well as lower cognitive performance. Conversely, hypoconnectivity was associated with less severe disease.

DISCUSSION: DM1 patients are characterized by brain hyperconnectivity and a less efficient brain network organization. Hyperconnectivity is discussed as a compensatory mechanism and is suggested as a disease severity marker.

PMID:39973452 | DOI:10.1177/22143602241307197

BMC Psychiatry. 2025 Feb 19;25(1):147. doi: 10.1186/s12888-025-06535-7.

ABSTRACT

BACKGROUND: This study focused on the relationship between facial working memory and resting-state brain function abnormalities in patients with schizophrenia.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 28 first-episode schizophrenia (FSZ) patients and 33 healthy controls (HCs). Degree centrality (DC) and Granger causality analysis (GCA) were used to assess brain region connectivity. A delayed matching-to-sample task was used to examine visual working memory for faces and houses. Correlations between DC and facial working memory accuracy were analysed. Brain regions were selected as regions of interest (ROIs) and subjected to further GCA. MRI signals of the DC or GCA were extracted and analysed for correlation with clinical symptom scores.

RESULT: The results revealed that FSZ patients presented facial working memory impairments at high loads (t = 2.21, P = 0.03). DC values of the right middle frontal gyrus (MFG) were linked to facial working memory accuracy (P < 0.05, false discovery rate (FDR) correction). GCA indicated inhibited connectivity from the right MFG to the right inferior frontal gyrus (IFG) and right thalamus and from the right postcentral gyrus to the right MFG in FSZ patients (P < 0.05, FDR correction). The DC values of the right thalamus were correlated with negative symptom scores (r = -0.44, P = 0.02) and affective symptom scores (r = -0.57, P < 0.01).

CONCLUSIONS: Our findings suggest that FSZ patients may have impaired facial working memory ability, which may be associated with altered functions in multiple brain regions. Some of these functions are associated with clinical symptoms, which may provide insight into the underlying neural mechanisms of schizophrenia.

PMID:39972263 | DOI:10.1186/s12888-025-06535-7