Most recent paper

Alzheimers Dement. 2025 Dec;21 Suppl 2:e107508. doi: 10.1002/alz70856_107508.

ABSTRACT

BACKGROUND: Type 2 diabetes mellitus is a risk factor for incident dementia including Alzheimer's disease (AD) dementia, but the mechanism is not clear. Abnormalities in brain default mode network (DMN) connectivity are associated both with T2DM and with increased risk for AD dementia. We explore the response of the DMN to acute glucose ingestion in diabetic versus non-diabetic older adults to better understand the brain's neurovascular response to glucose and ultimately relate that response to longitudinal cognitive and brain changes.

METHOD: We scanned (3T Siemens Prisma MRI) N = 59 cognitively unimpaired older (50-65 years) adults (N = 26 with T2DM, N = 33 non-diabetic). Each received a fasting resting-state functional MRI (rs-fMRI) scan, a 75g glucose tolerance test, a two-hour break and then a post-glucose rsfMRI scan. Rs-fMRI analyses were performed using FSL software with a posterior cingulate cortex/precuneus seed to identify the DMN in each participant. First, we investigated individual participant differences in DMN connectivity between fasting and 120 minutes post-glucose states to evaluate where each participant had more or less DMN connectivity post-glucose than while fasting. We then compared those individual differences in the brains' response to glucose within and between diabetic and non-diabetic groups. All analyses were adjusted for age and sex. We corrected for voxel-wise multiple comparisons (cluster-wise threshold z>3.1; corrected p-threshold = 0.001).

RESULT: We found significant differences between diabetic and non-diabetic participants in the brains' response to glucose ingestion broadly throughout the brain (Figure 1). Within-group analyses provided context for those differences. In diabetic participants, glucose ingestion caused an increase in DMN connectivity with the cerebellum and superior frontal gyrus but a decrease with parietal, occipital, and orbitofrontal regions. In contrast, in non-diabetic participants, glucose ingestion caused in increase in DMN connectivity with the lateral occipital cortex and middle temporal gyrus, but a decrease in connectivity with the cerebellum, medial prefrontal cortex, and anterior thalamus.

CONCLUSION: Our findings highlight distinct DMN connectivity responses to glucose ingestion in older adults with and without T2DM. Relating these differences to longitudinal brain and cognitive changes will help elucidate possible mechanisms and uses of rs-fMRI connectivity as a predictive biomarker.

PMID:41518670 | DOI:10.1002/alz70856_107508

J Neurol. 2026 Jan 10;273(1):76. doi: 10.1007/s00415-025-13613-z.

ABSTRACT

BACKGROUND: Amnestic mild cognitive impairment (aMCI) represents a prodromal stage of Alzheimer's disease and carries a high risk of progression to dementia. Disruptions in functional brain networks, synaptic degeneration, and tau pathology each serve as neural markers of cognitive decline in aMCI. In this study, we employed a multimodal neuroimaging approach to determine whether integrating these markers provides a more powerful explanation of cognitive deterioration than examining them individually.

METHODS: Twenty-eight aMCI patients and 24 healthy controls underwent cognitive assessment, resting-state fMRI, 11C-UCB-J PET (synaptic density), and 18F-MK-6240 PET (tau). Eighteen aMCI patients were reassessed after 2 years. Graph-theoretical measures of network topology were derived, and linear regression models were used to examine whether combining functional, synaptic, and tau markers improved prediction of cognitive decline in aMCI.

RESULTS: Longitudinal changes in right hippocampal characteristic path length, synaptic density, and tau accumulation jointly predicted decline in delayed memory recall, achieving a higher predictive performance (adjusted R2 = 0.753) than unimodal models (adjusted R2 range: - 0.009-0.421), with reduced overfitting degree.

CONCLUSIONS: Multimodal neuroimaging integrating functional network topology, synaptic density, and tau burden improves prediction of memory decline in aMCI. These findings highlight complementary neural processes underlying progression and support multimodal imaging as a valuable approach for monitoring in prodromal Alzheimer's disease.

PMID:41518411 | DOI:10.1007/s00415-025-13613-z

Hum Brain Mapp. 2026 Jan;47(1):e70450. doi: 10.1002/hbm.70450.

ABSTRACT

Research on sex differences in the brain is essential for a better understanding of how the brain develops and ages, and how neurological and psychiatric conditions can impact men and women differently. While numerous studies have focused on sex differences in brain structures, few have examined the characteristics of functional networks, particularly the language network. Although previous research suggests similar overall language performance across sexes, differences may still exist in the brain networks that underlie language processing. In addition, prior studies on sex differences in language have predominantly relied on task-based fMRI, which may fail to capture subtle differences in underlying functional activity. In this study, we applied a machine learning approach to classify participants' sex based on resting-state functional connectivity patterns of the language network in healthy young adults (270 men and 288 women; age: 22-36 years), and to identify the most predictive functional connectivity features. The classifier achieved 91.3% accuracy, with key discriminant features anchored to the left opercular part of the inferior frontal gyrus, the left planum temporale, and the left anterior middle temporal gyrus. These regions show distinctive connectivity patterns with heteromodal association cortices, including the occipital poles, angular gyrus, posterior cingulate gyrus, and intraparietal sulcus. Although there was an overlap between men and women, men displayed stronger functional connectivity values in these regions. These findings highlight sex-related differences in functional connectivity patterns of the language network at rest, underscoring the importance of considering sex as a variable in future research on language and brain function.

PMID:41518149 | DOI:10.1002/hbm.70450

J Neuroimaging. 2026 Jan-Feb;36(1):e70118. doi: 10.1111/jon.70118.

ABSTRACT

BACKGROUND AND PURPOSE: Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, hypersensitivity, and cognitive impairments. Alterations in brain functional connectivity have been suggested as possible mechanisms underlying pain amplification in these patients. This study aimed to investigate patterns of brain functional connectivity in patients with FM using resting-state functional magnetic resonance imaging.

METHODS: Data were obtained from the public OpenNeuro repository and acquired on a 3 Tesla scanner. The sample consisted of 33 women with a clinical diagnosis of FM (x̅ = 41.73 ± 6.09 years) and 33 age-matched healthy controls (x̅ = 41.52 ± 6.03 years), with no significant differences in age (p = 0.89) or education level (p = 0.81). Images were processed and analyzed using independent component analysis. Between-group comparisons were corrected for multiple comparisons using false discovery rate (FDR) correction (p < 0.05).

RESULTS: Patients with FM showed a significant reduction in functional connectivity within the right sensorimotor network (SMN) compared to controls (p-FDR < 0.05). Moreover, a negative correlation was observed between connectivity in this network and the sensory dimension of pain assessed by the McGill Pain Questionnaire (r = -0.35; p = 0.05).

CONCLUSION: The reduced functional connectivity within the SMN may represent a neurobiological marker of FM, reflecting dysfunctions in sensorimotor integration and central modulation of pain. These findings support the hypothesis that FM involves functional brain alterations related to pain perception and amplification.

PMID:41518021 | DOI:10.1111/jon.70118

Alzheimers Dement. 2025 Dec;21 Suppl 2:e105624. doi: 10.1002/alz70856_105624.

ABSTRACT

BACKGROUND: Growing evidence indicates that the coexistence of visual and auditory impairments increases the risk of developing Alzheimer's disease (AD). However, the mechanisms through which these sensory deficits influence the progression of AD, particularly their impact on amyloid and tau pathology, remain unclear. We hypothesize that alterations in the audio-visual dynamic network play a critical role in mediating the spread of amyloid-related tau pathology during the early stages of AD.

METHOD: This study included multimodal imaging data, including functional MRI, [18F]NAV4694 amyloid-PET, and [18F]NAV4694 tau-PET, from the TRIAD cohort (n = 216, Table 1). Participants were classified as amyloid-beta (Aβ) positive (A+) or negative (A-) based on established global uptake values of [18F]NAV4694 (global standardized uptake value ratio [SUVR] > 1.55). Tau positivity (T+) or negativity (T-) was determined using [18F]MK6240, with a temporal meta-ROI SUVR threshold > 1.30. Tau staging was based upon Braak stage classification. Brain dynamics in resting-state fMRI data were analyzed with a multilayer modularity algorithm in MATLAB, focusing on primary sensory and higher-order networks.

RESULT: Module allegiance within the auditory network (AN) and visual networks (VN) was lower in the A+T+ group compared to the A-T- group. Additionally, flexibility within the frontoparietal network (FPN) was increased, while recruitment within the FPN and integration between AN and VN were reduced in the A+T+ group compared to A-T- group (Figure 1). Integration between AN and VN negatively correlated with [18F]MK6240 SUVR in Braak stage 1 through 5 and the temporal meta-ROI, as well as with neocortical [18F]NAV4694 SUVR. Furthermore, AN-VN integration mediated the relationship between neocortical [18F]NAV4694 SUVR and [18F]MK6240 SUVR in Braak stage 1 and 2 (Figure 2).

CONCLUSION: Our study suggests that audio-visual network integration during the early stages of tau pathology mediates amyloid-related tau accumulation. This supports a framework in which decline brain network integration may facilitates the early spread of amyloid-driven tau pathology across interconnected brain regions.

PMID:41513243 | DOI:10.1002/alz70856_105624

Alzheimers Dement. 2025 Dec;21 Suppl 2:e107675. doi: 10.1002/alz70856_107675.

ABSTRACT

BACKGROUND: Disruptions in brain network connectivity are strongly associated with the progression of cognitive decline in the Alzheimer's disease (AD) continuum, including mild cognitive impairment (MCI). This study aimed to investigate the relationship between alterations in functional brain connectivity within the default mode network (DMN) in patients with MCI and plasma biomarker levels typically altered in AD (Aβ40, Aβ42, Tau, pTau-181, Aβ42/Aβ40, Aβ42/pTau, Aβ42/tTau, pTau/tTau).

METHODS: Eighteen patients (mean age = 65 years) diagnosed with MCI according to the 2018 NIA-AA and Alzheimer's Association criteria, based on medical and neuropsychological evaluation at the Hospital das Clínicas, University of Campinas (HC-UNICAMP), Brazil, were selected for blood collection and subsequent functional magnetic resonance imaging (fMRI) scans. Plasma samples were stored and analyzed using the automated SIMOA HD-X immunoassay system (Quanterix, Billerica, MA). Resting-state fMRI (RS-fMRI) data were acquired using a 3T Achieva-Intera PHILIPS® scanner. Both imaging data and correlation analyses were processed using the UF2C toolbox within MATLAB and SPM12, with results corrected for false discovery rate (FDR).

RESULTS: Two notable negative correlations were found between the right hippocampus and right precuneus and the Aβ42/Aβ40 ratio (Spearman's correlation: r = -0.76, p = 0.033). No significant correlations were observed for other plasma biomarkers after FDR correction.

CONCLUSION: Since network reorganization is a characteristic feature of MCI and AD, with regions exhibiting increased or decreased activity, the observed inverse relationship between Aβ42/Aβ40 and functional connectivity between the right hippocampus and right precuneus (indicating that an increase in Aβ42/Aβ40 is associated with decreased functional connectivity, and vice versa) supports the disease's underlying pathophysiology. This finding provides a potential avenue for research and diagnostic monitoring. Further studies are needed to explore the functional impact of these alterations and their relevance to disease progression.

PMID:41512461 | DOI:10.1002/alz70856_107675

Sleep Med. 2026 Jan 7;139:108769. doi: 10.1016/j.sleep.2026.108769. Online ahead of print.

ABSTRACT

The locus coeruleus (LC) contains norepinephrine (NE)-synthesizing neurons that release NE throughout the brain and plays a critical role in maintaining arousal and attention. However, it remains unclear whether resting-state functional connectivity (FC) of the LC during rested wakefulness (RW) is related to attention decline following acute sleep deprivation (SD). In this study, thirty healthy participants with normal sleep patterns underwent resting-state functional magnetic resonance imaging (fMRI) before and after acute SD. Attention performance was assessed using the psychomotor vigilance task (PVT). Results showed that FC between the LC and the left caudal temporal thalamus significantly increased after SD, and LC-thalamus FC during RW was correlated with both attention performance and attention decline after acute SD. However, these LC-thalamus-attention associations were primarily observed when global signals regression (GSR) was applied during preprocessing. Furthermore, LC-derived FC patterns were associated with the distribution of the NE transporter from a public dataset. Sensitivity analyses indicated that the observed relationships between LC-thalamus FC and attention were dependent on the preprocessing choice of GSR, although similar effects were also observed in other thalamic subregions. Together, these pilot results suggest a potential LC-thalamus-attention mechanism associated with sleep loss and provide a testable framework for future studies with optimized LC imaging and analytical approaches.

PMID:41512349 | DOI:10.1016/j.sleep.2026.108769

JMIR Res Protoc. 2026 Jan 9;15:e67132. doi: 10.2196/67132.

ABSTRACT

BACKGROUND: Nasyakarma, an Ayurveda nasal drug delivery system, is considered a potent therapeutic modality in panchakarma treatment. Uniquely, nasal drug delivery can bypass liver metabolism and the blood-brain barrier for faster drug delivery. Available studies on nasya karma focus mainly on its efficacy. This pioneering study aims to explore the mechanisms of nasya karma on brain function and neurophysiology, investigating its potential to modulate activity in specific brain regions and affect the functional connectivity between these regions using functional magnetic resonance imaging (fMRI).

OBJECTIVE: The study aims to map the neurophysiological response of the brain to nasya karma using blood oxygen level-dependent fMRI in both rest and task phases and assess the impact of nasya karma on quality of life, cognition, sleep, and psychological well-being in healthy volunteers.

METHODS: A total of 60 healthy volunteers in the age group of 20 to 40 years who fulfill the selection criteria will be recruited for this randomized controlled trial at the National Ayurveda Institute for Panchakarma, Cheruthuruthy, Kerala, India, and randomized in a 1:1 ratio to either the intervention group (n=30; group 1, receiving nasya karma with anu taila, an oil-based formulation manufactured using a paste and decoction of herbal medicines with oils as the base material, for a period of 14 days) or the control group (n=30; group 2, not receiving any intervention). The participants will undergo task-based and resting fMRI on day 1 (twice on day 1 before administration and 15 minutes after nasya karma for participants in group 1) and day 14 to map the neurophysiological response to nasya karma of the brain. A comprehensive neuroimaging protocol using structural magnetic resonance imaging and fMRI will be used in the study. The effect of nasya karma on sleep, psychological well-being, cognition, and quality of life will be assessed on the 1st and 30th days in both groups.

RESULTS: After the data collection process of this randomized controlled trial is completed, the study will go into the analysis stage, in which the collected data will be subjected to robust statistical analysis. The final results of the study are expected to be published in 2026.

CONCLUSIONS: This study will investigate the neurophysiological mechanisms of nasya karma by examining clinical, neuropsychological, and neuroimaging variables to identify associated neural patterns to develop therapeutic protocols for many diseases. The findings will also provide evidence for future research supporting the use of nasya karma as a practical and noninvasive therapeutic modality for treating cerebrovascular, behavioral, and neurological disorders as indicated in Ayurveda. The study will propel innovative research focusing on the neural mechanisms responsible for the delivery of central nervous system therapeutics to the brain, thereby bypassing the blood-brain barrier and yielding favorable outcomes in central nervous system diseases.

TRIAL REGISTRATION: Clinical Trials Registry - India CTRI/2023/06/054219; hhttps://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=ODQ1OTU=&Enc=&userName=.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67132.

PMID:41511827 | DOI:10.2196/67132

J Speech Lang Hear Res. 2026 Jan 9:1-17. doi: 10.1044/2025_JSLHR-25-00053. Online ahead of print.

ABSTRACT

PURPOSE: With growing interest in modeling neurobehavior, there is increased interest in understanding patterns of functional connectivity (FC) during language processing. Previous research has suggested that static resting-state functional magnetic resonance imaging (rs-fMRI) and task-based functional magnetic resonance imaging (tb-fMRI) may be interchangeable in determining FC in language-related regions of interest. Authors have argued for the elimination of using tb-fMRI assessments in preoperative clinical workup of language mapping. However, given that language exhibits not only 3D spatial attributes but also temporal components, understanding the temporal dynamics is essential in developing adaptive computational models. Thus, the stability of language neural networks during rs-fMRI and tb-fMRI during auditory comprehension was examined in healthy participants.

METHOD: Twenty-three participants underwent rs-fMRI and 12 participants underwent tb-fMRI while listening to an auditory description task. Sliding scale time series correlation was used to generate estimates of dynamic FC. We used Spearman correlation with Bonferroni correction to compute statistically significant whole-brain dynamic networks. A total of 59,831 data points (42,159 in rs-fMRI, 10,152 in tb-fMRI, and 7,520 in overlap) were analyzed.

RESULTS: There was more notable fluctuation in linear correlation over time for dynamic FC networks activated during the task relative to baseline than during rs-fMRI. Differences in dynamic FC were noted in only specific common networks in the left hemisphere during tb-fMRI.

CONCLUSIONS: The temporal dynamics of identical neural networks during rs-fMRI and tb-fMRI are markedly different, especially within the left hemisphere. This may be an important computational model feature that may improve model prediction of clinical outcomes following central nervous system injury.

PMID:41511194 | DOI:10.1044/2025_JSLHR-25-00053

Neurorehabil Neural Repair. 2026 Jan 9:15459683251395692. doi: 10.1177/15459683251395692. Online ahead of print.

ABSTRACT

BackgroundUnderstanding individual variability in response to interventions is essential for developing personalized treatment strategies. In rare and clinically heterogeneous conditions like primary progressive aphasia (PPA), predicting treatment response is particularly challenging due to varying clinical manifestations. In this study, we aimed to identify and analyze predictors of individual language response to transcranial direct current stimulation (tDCS) of the left inferior frontal gyrus (IFG), using a novel, robust analytic approach focused on treatment effect heterogeneity.MethodsWe compared the ability of predicting individual effect (active vs sham tDCS during 20-minute sessions on weekdays for 3 weeks; active: 2 mA current across electrodes; sham: current ramped down after 30 seconds), using demographic and clinical patient characteristics (eg, PPA variant and disease progression, baseline language performance) or volumetric fMRI data versus functional connectivity (from resting-state fMRI) in the cohort of 36 patients.ResultsFunctional connectivity alone had the highest predictive value for outcomes, explaining 62% of the variance of the tDCS effect in generalization (semantic fluency) and 75% of the main outcome (written naming), contrasted with <15% (for semantic fluency) and <23% (for written naming) of variance predicted by demographic and clinical patient characteristics or volumetric data. Patients with higher baseline functional connectivity within the left IFG (between pars opercularis and pars triangularis) were most likely to benefit from tDCS both in generalization (semantic fluency) as well as in the main outcome (written naming). In addition, patients with higher baseline FC between the middle temporal pole and superior temporal gyrus, were most likely to show generalization effects of tDCS.ConclusionsThe present study showcases the importance of a baseline functional connectivity scan in predicting tDCS outcomes, and points toward a precision medicine approach in neuromodulation studies. The study has important implications for clinical trials and practice, providing a statistical method that addresses heterogeneity in patient populations and allowing accurate prediction and enrollment of those who will most likely benefit from specific interventions.

PMID:41510899 | DOI:10.1177/15459683251395692

bioRxiv [Preprint]. 2026 Jan 2:2026.01.02.697199. doi: 10.64898/2026.01.02.697199.

ABSTRACT

Intrinsic brain activity is characterized by large-scale spatiotemporal patterns that underpin functional connectivity and cognition. Quasi-periodic patterns (QPPs) and complex principal component analysis (cPCA) have emerged as reproducible methods for capturing spatiotemporal network interactions in resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods remain sensitive to methodological factors such as scan duration, repetition time (TR), and frequency band selection. This study systematically evaluates how these parameters influence the stability and reliability of QPP- and cPCA-derived functional connectivity patterns across multiple datasets. Using five independent rs-fMRI datasets, we evaluate the impact of scan length on pattern reliability, explore the effects of TR on spatiotemporal patterns, and compare the sensitivity of different frequency bands (Slow-5, Slow-4, infraslow) in capturing network dynamics. Our findings reveal that while both QPPs and cPCA detect intrinsic network activity, their reliability varies with acquisition parameters. QPPs exhibit greater stability in shorter scans, making them suitable for individual-level analyses, whereas cPCA provides a broader representation of phase-coherent fluctuations but shows greater between-subject variability and benefits more from longer, group-level acquisitions. Additionally, frequency band selection significantly influences the temporal structure of extracted patterns: in our analyses, Slow-5 (0.01-0.027 Hz) tended to emphasize more recurrent, synchronized network configurations, whereas Slow-4 (0.027-0.073 Hz) more often revealed transitions between connectivity states. These results provide critical insights into optimizing methodological choices for dynamic functional connectivity analysis, enhancing the interpretability of spatiotemporal patterns in both basic and clinical neuroimaging research.

PMID:41509284 | PMC:PMC12776408 | DOI:10.64898/2026.01.02.697199

Alzheimers Res Ther. 2026 Jan 9. doi: 10.1186/s13195-026-01956-2. Online ahead of print.

NO ABSTRACT

PMID:41507977 | DOI:10.1186/s13195-026-01956-2

Alzheimers Dement. 2025 Dec;21 Suppl 2:e107126. doi: 10.1002/alz70856_107126.

ABSTRACT

BACKGROUND: Time-frequency analysis of resting-state fMRI (rs-fMRI) is essential for uncovering intrinsic frequency and amplitude characteristics. Mean energy and frequency profiles of different resting-state networks (RSNs) can provide fundamental information about brain activity and its impairment in aging, and characterize stage-specific alterations across the Alzheimer's disease (AD) continuum: cognitively normal (CN), mild cognitive impairment (MCI), and AD.

METHOD: Using the ADNI database (adni.loni.usc.edu), a total of 297 fMRI sessions from 150 participants (all positive for amyloid PET) were included in this study. We determined RSNs using standard group ICA software. Then, using Empirical Mode Decomposition (EMD), all RSN time series were decomposed into intrinsic mode functions (IMFs). Only the first 4 IMFs that spanned a frequency range above 0.01 Hz were used for the characterization of the RSNs. We estimated energy and frequency measures to characterize our 3 groups.

RESULT: With respect to the mean energy profiles, we found significantly reduced energy in the diseased groups (MCI and AD) in IMF2 and IMF3 of many RSNs with reference to the controls. For certain RSNs, specifically, frontoparietal; visual; temporal; the IMF4 showed the opposite trend (with large effect size, Cohen's d>0.8) with the AD group having increased mean energy compared to the other two groups. In terms of the frequency profiles a similar increase in the mean frequency was observed for IMF2, IMF3 and IMF4 in the AD and MCI patients with respect to the controls.

CONCLUSION: We found that MCI and AD participants showed reduced energy and increased frequency in many RSNs. In particular, the DMN network (DMN1) showed the largest group difference in energy and frequency for IMF2 and IMF3. This is consistent with the fact that low-frequency power (<0.1 Hz) is more related with cognitive function, while high-frequency power (>0.1 Hz) is more associated with physiological activity. From a clinical perspective the decreased energy shown by the AD patients for many RSNs highlights the potential of fMRI in capturing differences between diagnostic groups. Specifically, these variables could act as potential biomarkers that models the characteristic changes in different brain networks along the AD continuum.

PMID:41506779 | DOI:10.1002/alz70856_107126

Psychiatry Res Neuroimaging. 2025 Dec 31;357:112118. doi: 10.1016/j.pscychresns.2025.112118. Online ahead of print.

ABSTRACT

BACKGROUND: Postpartum depression (PPD) is a common women's psychological health issue. While studies have identified regional functional abnormalities, the global functional topological alterations associated with PPD remain to be fully characterized. This study aims to investigate the alteration of functional topological properties in PPD patients.

METHODS: Resting-state functional MRI (rs-fMRI) was acquired from 30 PPD patients, 23 healthy pregnant women (HPW), and 26 healthy non-pregnant women (HC). Functional brain networks were constructed using inter-regional Pearson's correlation coefficient and analyzed via graph theory. Machine learning was applied to the functional connectome to distinguish PPD from HPW.

RESULTS: Compared to HC and HPW, the PPD group showed a shift toward a more regularized network topology in functional brain network. In comparison with HC, PPD had altered topological properties mainly in the salience network (SN, e.g., left insula) and associated subcortical regions (e.g., amygdala), while HPW exhibited functional differences mainly within the default mode network (DMN). Abnormal regions (e.g., pallidum, precuneus) between PPD and HPW correlated with depression severity. Combining machine learning with functional connectivity metrics predicted PPD with 88 % accuracy.

CONCLUSION: Pregnancy may alter the functional connectome in DMN, and postpartum depression may disrupt the connectivity in SN. The insula and precuneus are critical for identifying PPD and HPW. These findings suggest that functional connectome alterations are clinical significant and may facilitate the timely clinical detection of PPD.

PMID:41506130 | DOI:10.1016/j.pscychresns.2025.112118

Neuroimage Clin. 2026 Jan 4;49:103942. doi: 10.1016/j.nicl.2026.103942. Online ahead of print.

ABSTRACT

OBJECTIVES: Understanding the progression mechanisms of glioma holds significant implications for improving clinical management. However, the natural progression patterns of glioma remain poorly understood due to the lack of longitudinal clinical samples from untreated patients.

MATERIALS AND METHODS: In this study, we systematically explored the natural progression trajectory of glioma by combining functional magnetic resonance imaging (fMRI) analysis of 24 rare multifocal glioma patients with bioinformatic analysis of single-cell RNA sequencing (scRNA-seq) data obtained from tumor samples of glioma mouse with early, mid, and endpoint lesions.

RESULTS: We discovered that larger tumors in multifocal gliomas exhibit stronger functional connectivity with the cerebral cortex and higher degree centrality within brain networks. ScRNA-seq of longitudinal mouse glioma samples revealed progressive activation of synaptic organization and associated regulatory pathways during the natural progression of glioma.

CONCLUSION: Our multimodal, cross-scale study demonstrates that the natural progression pattern of glioma macroscopically manifests as functional hyperconnectivity with the cerebral cortex, which is supported by microscale molecular programs driving synaptogenesis. These findings elucidate the characteristics and mechanisms underlying glioma natural progression.

PMID:41506055 | DOI:10.1016/j.nicl.2026.103942

Magn Reson Med. 2026 Jan 8. doi: 10.1002/mrm.70248. Online ahead of print.

ABSTRACT

PURPOSE: Functional MRI (fMRI) in awake rodents presents countless valuable opportunities for researchers to probe questions that may not be accessible through anesthetized models, such as voluntary locomotion. The commonly used echo planar imaging (EPI) sequence is highly sensitive to motion that occurs even outside of the imaging plane. Recently, zero echo time sequences have been adopted for fMRI to address this challenge.

METHODS: This study proposes a robust and reproducible protocol for longitudinal imaging of awake mice during spontaneous locomotion, using an implanted headpiece, incremental training, zero TE fMRI, reinforcement learning, and a custom treadmill module. Locomotion is known to have wide-ranging effects on brain activity and can alter neurovascular coupling, making it critical to understand this aspect of natural behavior.

RESULTS: We present results from 10 trained mice across three different fMRI scanning sessions, finding minimal head motion across scans (average framewise displacement matched anesthetized EPI (p > 0.05)), consistent resting-state functional connectivity across subjects and scans, and evidence of a minimal stress response at the group and individual level. We also demonstrate little difference on signal quality during locomotion and altered functional connectivity and spatiotemporal dynamics during locomotion compared to rest.

CONCLUSIONS: This work establishes a new benchmark for awake rodent fMRI, enabling the direct investigation of naturalistic behaviors like locomotion and their whole-brain correlates without the confounding effects of anesthesia or excessive restraint.

PMID:41505251 | DOI:10.1002/mrm.70248

J Integr Neurosci. 2025 Dec 25;24(12):43660. doi: 10.31083/JIN43660.

ABSTRACT

BACKGROUND: Disorders of consciousness (DoCs) following traumatic brain injury (TBI), or cerebrovascular disease (CVD) are difficult to prognose, as reliable biomarkers are lacking. Resting-state functional magnetic resonance imaging (fMRI) amplitude of low-frequency amplitude (ALFF) may capture etiology-specific neural activity, but its prognostic value for spinal cord stimulation (SCS) outcomes remains unknown. In this study we therefore investigated etiology-specific ALFF patterns in TBI- and CVD-induced DoCs and evaluated their prognostic value for recovery after SCS.

METHODS: Resting-state fMRI data from patients with TBI (n = 16) and CVD (n = 15), and healthy controls (n = 12), were analyzed. Whole-brain ALFF differences were also compared between the groups. Correlations between ALFF and 6-month post-SCS Coma Recovery Scale-Revised (CRS-R) score improvements were assessed. Logistic regression was used to identify consciousness recovery markers.

RESULTS: Compared with healthy controls, patients with TBI demonstrated a significant increase in ALFF within the bilateral insula, thalamus, and brainstem (p < 0.05), suggesting compensatory neural hyperactivity potentially involving glutamatergic pathways. Patients with CVD exhibited elevated ALFF in the contralateral sensorimotor cortex (p < 0.05), indicating ipsilateral neural reorganization. Notably, the thalamic ALFF were strongly correlated with consciousness recovery, as measured by improvements in CRS-R score at 6 months in both the TBI (r= 0.64, p = 0.0071) and CVD (r = 0.59, p = 0.02) groups. Furthermore, logistic regression analysis identified increased ALFF in the anterior cingulate cortex-thalamic loop (odds ratio [OR] = 3.21, p < 0.05) as a potential cross-etiology biomarker for recovery following SCS.

CONCLUSIONS: ALFF reveal distinct neuroplasticity mechanisms, including compensatory activation in TBI and ipsilateral reorganization in CVD. Elevated anterior cingulate cortex (ACC)-thalamic ALFF are a key cross-etiology biomarker for consciousness recovery to guide SCS target selection.

PMID:41503991 | DOI:10.31083/JIN43660

medRxiv [Preprint]. 2026 Jan 2:2025.12.31.25343265. doi: 10.64898/2025.12.31.25343265.

ABSTRACT

BACKGROUND: Adolescence is a period of rapid neurobiological and behavioural change, yet it remains unclear how deviations from normative brain maturation relate to the development of internalising and externalising symptoms.

METHODS: Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we combined multimodal brain age prediction with bivariate latent growth curve (BLGC) models to test whether deviations in brain maturation - indexed by the brain age gap (BAG) - relate to mental health development across late childhood and adolescence. Brain age was estimated from T1-weighted, diffusion MRI (dMRI), and resting-state fMRI (rs-fMRI) data across four MRI waves (ages ~8.3-17.5). Internalising and externalising symptoms were assessed across ten waves with the self-report Brief Problem Monitor (BPM).

RESULTS: Across T1, dMRI, and multimodal models, deviations from age-expected brain maturation and internalising and externalising symptoms showed coordinated nonlinear development across adolescence. Adolescents whose brains increasingly diverged from age-expected maturation over time also showed accelerating symptom trajectories. These associations were small to moderate in magnitude and were most consistent for internalising symptoms in females (r = .15-.23), whereas externalising symptoms showed broader but less selective nonlinear associations across modalities (r = .15-.32). Linear and intercept-level associations were weaker (r = .06-.11) and modality-specific. Formal tests provided no evidence for robust sex differences in these associations after correction for multiple comparisons.

CONCLUSION: These results indicate that adolescent mental health problems are most strongly linked to nonlinear changes in how individuals diverge from age-expected brain trajectories, rather than to fixed differences in brain age. Nonlinear shifts in maturational tempo may therefore be a key developmental feature underlying vulnerability to psychopathology in youth.

PMID:41503487 | PMC:PMC12772674 | DOI:10.64898/2025.12.31.25343265

Neuroimage Rep. 2025 Dec 17;6(1):100304. doi: 10.1016/j.ynirp.2025.100304. eCollection 2026 Mar.

ABSTRACT

Understanding alterations in functional connectivity among individuals with substance use disorder (SUD) is critical for elucidating the neural mechanisms underlying addiction. In this study, we applied Energy Landscape Analysis (ELA), an energy-based machine learning method, to examine whole-brain functional connectivity differences between SUD patients and healthy controls (HCs). A key methodological challenge in ELA lies in the selection of appropriate Regions of Interest (ROIs) from comprehensive brain atlases. To address this, we employed seed-based connectivity analysis to identify task-relevant ROIs, thereby overcoming the limitation of focusing on a restricted subset of regions. The dataset comprised 53 cocaine users (CUs) and 52 age- and sex-matched HCs, with functional MRI data preprocessed using the CONN toolbox. ROI-to-ROI seed-based connectivity was computed through first- and second-level analyses. ELA revealed that HCs exhibited stronger positive connectivity between cerebellar and visual regions, whereas CUs showed stronger positive connectivity between the cerebellum and the inferior temporal gyrus (temporooccipital part; toITG). Seven low-energy connectivity states were identified that differentiated the two groups. In these states, the cerebellum and toITG demonstrated antagonistic activation patterns, while the cerebellum and visual cortex co-activated in HCs. Temporal dynamics analyses further indicated that HCs visited these low-energy states more frequently, driven by shorter dwell times but higher transition rates. These findings suggest that cocaine addiction may reflect a weakening of adaptive, protective ("guardian") connectivity patterns, rather than an increased propensity to remain in maladaptive connectivity states. Collectively, these results highlight key network-level distinctions between HCs and CUs and offer new insights into the neurobiological mechanisms of cocaine addiction.

PMID:41503436 | PMC:PMC12771301 | DOI:10.1016/j.ynirp.2025.100304

Alzheimers Dement (N Y). 2026 Jan 5;12(1):e70198. doi: 10.1002/trc2.70198. eCollection 2026 Jan-Mar.

ABSTRACT

INTRODUCTION: Ultrasound neuromodulation has emerged as a promising adjunctive therapy in Alzheimer's disease (AD), yet a controversial issue remains: whether its reported long-term therapeutic effects could be attributed to potential confounds rather than genuine neuromodulatory mechanisms. Although auditory confounds via air- or bone-conducted sound have been discussed for immediate effects, their relevance for enduring therapeutic outcomes-essential for clinical application-remains unknown. This exploratory study is the first to examine whether long-term cognitive and neural effects of transcranial pulse stimulation (TPS) in AD are linked to persistent auditory network activation.

METHODS: A comprehensive re-analysis of task-based and resting-state functional magnetic resonance imaging (fMRI) data was conducted using data from the currently largest sham-controlled clinical ultrasound neuromodulation study (Matt et al., 2025). To isolate possible auditory contributions, we applied a contrast-based framework targeting (1) air-conducted sound, (2) combined air- and possibly bone-conducted sound, and (3) bone-conduction-specific effects. Analyses included: (a) task-based auditory cortex co-activation, (b) functional connectivity between auditory and dorsal attention networks (the latter was modulated in the original study), (c) global efficiency within the auditory network, and (d) correlations with neuropsychological test battery scores.

RESULTS: No significant long-term activation of auditory cortices was observed in task-based fMRI. Resting-state analyses showed no altered connectivity between auditory and attention networks, no changes in auditory network global efficiency, and no associations between auditory metrics and cognitive performance. Effect-size estimates were small, and 95% confidence intervals placed conservative upper bounds that argue against sizeable, sustained auditory confounds. These findings were consistent across all contrast conditions.

CONCLUSION: Using data from a rigorously controlled cognitive trial, we found no evidence of long-term auditory network effects following TPS. This makes it unlikely that auditory confounds are a key factor underlying the cognitive network effects observed with long-term ultrasound neuromodulation in typical verum-sham settings as investigated here.

HIGHLIGHTS: Long-term transcranial pulse stimulation effects were evaluated for potential auditory confounds using functional magnetic resonance imaging (fMRI).Analyses included task-fMRI, resting-state functional connectivity, and auditory network efficiency.No auditory long-term effects were detected.Auditory confounds are unlikely to be the key factor for cognitive network effects.

PMID:41503119 | PMC:PMC12771597 | DOI:10.1002/trc2.70198