Most recent paper

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

Front Psychol. 2025 Feb 4;15:1405425. doi: 10.3389/fpsyg.2024.1405425. eCollection 2024.

ABSTRACT

INTRODUCTION: Most studies of dyslexia focus on domains of impairment (e.g., reading and phonology, among others), but few examine possible strengths. In the present study, we investigated semantic fluency as a cognitive strength in English-speaking children with dyslexia aged 8-13.

METHODS: Ninety-seven children with dyslexia completed tests of letter and semantic verbal fluency, standardized measures of reading and cognitive functions, and task-free resting-state functional magnetic resonance imaging (rs-fMRI). First, we adjusted performance on semantic fluency by letter fluency and created a residual score that was used to separate participants into high (residual >0) or average (residual <0) semantic performance groups. We then employed a psycholinguistic clustering and switching approach to the semantic fluency task and performed dynamic task-free rs-fMRI connectivity analysis to reveal group differences in brain dynamics.

RESULTS: High and average semantic fluency groups were well-matched on demographics and letter fluency but differed on their psycholinguistic patterns on the semantic fluency task. The high semantic fluency group, compared to the average semantic fluency group, produced a higher number of words within each cluster, a higher max cluster size, and a higher number of switches. Differential dynamic rs-fMRI connectivity (shorter average dwell time and greater brain state switches) was observed between the high and average groups in a large-scale bilateral frontal-temporal-occipital network.

DISCUSSION: These data demonstrate that a subgroup of children with dyslexia perform above average on semantic fluency tasks and their performance is strongly linked to distinct psycholinguistic patterns and differences in a task-free resting-state brain network, which includes regions previously implicated in semantic processing. This work highlights that inter-individual differences should be taken into account in dyslexia and reveals a cognitive area of strength for some children with dyslexia that could be leveraged for reading interventions.

PMID:39967994 | PMC:PMC11832474 | DOI:10.3389/fpsyg.2024.1405425

Int J Ophthalmol. 2025 Feb 18;18(2):297-307. doi: 10.18240/ijo.2025.02.14. eCollection 2025.

ABSTRACT

AIM: To analyze whether alterations of voxel mirror homology connectivity (VMHC) values, as determined by resting-state functional magnetic resonance imaging (rs-fMRI), occur in cerebral regions of patients with hypertensive retinopathy (HR) and to determine the relationship between VMHC values and clinical characteristics in patients with HR.

METHODS: Twenty-one patients with HR and 21 age-matched healthy controls (HCs) were assessed by rs-fMRI scanning. The functional connectivity between the hemispheres of the cerebrum was assessed by measuring VMHC, with the ability of VMHC to distinguish between the HR and HC groups assessed using receiver operating characteristic (ROC) curve analysis. Differences in the demographic and clinical characteristics of the HR and HC groups were analyzed by independent sample t-tests. The relationship between average VMHC in several brain areas of HR patients and clinical features was determined using Pearson correlation analysis.

RESULTS: Mean VMHC values of the bilateral cuneus gyrus (BA19), bilateral middle orbitofrontal gyrus (BA47), bilateral middle temporal gyrus (BA39) and bilateral superior medial frontal gyrus (BA9) were lower in the HR than in the HC group.

CONCLUSION: VMHC values can predict the development of early HR, prevent the transformation of hypertensive microangiopathy, and provide useful information explaining the changes in neural mechanism associated with HR.

PMID:39967983 | PMC:PMC11754017 | DOI:10.18240/ijo.2025.02.14

Transl Psychiatry. 2025 Feb 19;15(1):58. doi: 10.1038/s41398-025-03282-x.

ABSTRACT

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

PMID:39966397 | DOI:10.1038/s41398-025-03282-x

Brain Imaging Behav. 2025 Feb 18. doi: 10.1007/s11682-025-00982-2. Online ahead of print.

ABSTRACT

Lesions in the basal ganglia present different neuroimaging manifestations compared to other regions. The functional connectivity and white matter (WM) microstructural alterations in patients with left basal ganglia acute ischemic stroke (AIS) remain unknown. This study aimed to explore the alterations of functional connectivity and WM microstructure, as well as their relationship with cognitive performance in patients with left basal ganglia AIS. We acquired resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 41 individuals with left basal ganglia AIS and 41 healthy controls (HC). The degree centrality (DC) method was applied to calculate the functional connectivity and Tract-Based Spatial Statistics was employed to evaluate the voxel-based group differences of diffusion metrics for the values of fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), radial diffusivity, mean kurtosis (MK), axial kurtosis, and radial kurtosis (RK). AIS showed attenuated DC in the bilateral precuneus and enhanced DC in the left caudate nucleus, compared with HC. In AIS, DC in the left caudate nucleus correlated positively with the Montreal Cognitive Assessment (MoCA) score (r = 0.681, p < 0.05). AIS had significantly decreased FA, AD, MK, and RK in WM tracts, including the internal capsule (IC), genu of corpus callosum (CC), body of CC, left superior longitudinal fasciculus (SLF), left cerebral peduncle, left corticospinal tract, anterior corona radiata (ACR), and left cingulum gyrus (CG). The MK in a cluster including the body of CC, right IC, left cingulate, SLF, ACR, and left CG was also significantly negatively correlated with MoCA scores (r = -0.508, p < 0.05). This study revealed that left basal ganglia AIS not only disrupted the functional connectivity of the whole brain but also had a pervasive impact on the WM microstructure of the whole brain. These findings provide novel insights into the underlying neural mechanisms of early cognitive decline in patients after AIS.

PMID:39964657 | DOI:10.1007/s11682-025-00982-2

Childs Nerv Syst. 2025 Feb 18;41(1):116. doi: 10.1007/s00381-025-06774-9.

ABSTRACT

The role of connectivity in the function and development of the human brain has been intensely studied over the last two decades. These findings have begun to be translated to the clinical setting, particularly in the context of epilepsy. Determining connectivity in the epileptic brain can be challenging and is even more difficult in the pediatric patient. In pediatric epilepsy, resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful method for determining connectivity. Resting-state fMRI is a non-invasive method of determining correlated activity (functional connectivity) between brain regions in a task-free manner. This modality is especially useful in the pediatric population as it can be done under sedation and requires minimal cooperation from the patient. Over the last decade, rs-fMRI has been increasingly used and studied in pediatric epilepsy. In this article, we review this recent work and discuss the current state of rs-fMRI in the diagnosis and management of the different pediatric epilepsy syndromes. We first provide an overview of rs-fMRI in practice, including the different methods of analysis. We then describe the connectivity findings in pediatric epilepsy that have been revealed by rs-fMRI and the current state of rs-fMRI use in practice. Finally, we discuss what rs-fMRI has revealed about postoperative changes in connectivity and provide several recommendations for future research.

PMID:39964613 | DOI:10.1007/s00381-025-06774-9