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Alzheimers Dement. 2026 Jan;22(1):e70944. doi: 10.1002/alz.70944.

ABSTRACT

BACKGROUND: Cognitive impairments are a hallmark of Huntington's disease (HD).

METHODS: Seventy-one participants (43 HD gene-expanded [HDGE], 28 healthy controls) from the HD-Young Adult Study at two timepoints ≈ 4.7 years apart, completed the Cambridge Neuropsychological Test Automated Battery Rapid Visual Information Processing task and underwent resting-state functional magnetic resonance imaging. We focused on predefined regions of interest that are involved in sustained attention.

RESULTS: HDGE individuals showed significantly poorer sustained attention than controls (padj = 0.007), with no significant change over time. Functional connectivity (FC) analyses revealed group differences in attention-related networks, including the occipital-operculum and lentiform-orbitalis pathways. Time and group × time effects were also observed in frontal and parietal regions.

DISCUSSION: These findings demonstrate early and persistent attention deficits in HDGE, linked to altered FC in attention-related circuits. This supports the presence of early cognitive dysfunction in HD and highlights potential compensatory and pathological changes in brain networks prior to the onset of clinical motor symptoms.

HIGHLIGHTS: We detail the discovery of early sustained attention deficits in Huntington's disease (HD) gene-expanded (HDGE) young adults. These sustained attention deficits do not measurably decline over a 4.7-year period. Altered functional connectivity was observed in attention-related brain networks. Alterations in regions include occipital, opercular, lentiform, and frontal areas. Findings support attention as an early cognitive biomarker in HDGE young adults.

PMID:41528030 | DOI:10.1002/alz.70944

PeerJ. 2026 Jan 8;14:e20539. doi: 10.7717/peerj.20539. eCollection 2026.

ABSTRACT

The concept of emotional intelligence (EI) has gained significant interest in the scientific community in recent years. Despite its demonstrated impact on social and personal functioning, the neural bases underlying EI remain poorly understood. This study aimed to conduct a comprehensive systematic review of the existing literature on the neural correlates of EI. The search was conducted in Web of Science, Scopus, PsycINFO, and PubMed databases. A total of 849 studies were initially identified (after duplicates were removed), of which 34 met the inclusion criteria and were selected for the final synthesis. These studies employed various brain mapping techniques, including lesion studies, grey and white matter structural magnetic resonance imaging (MRI), task-based functional magnetic resonance imaging (fMRI), resting-state fMRI, and electroencephalogram (EEG). The findings of this review suggest that EI is supported by a complex and widespread brain network primarily implicated in the integration of cognitive and emotional processes, with significant involvement of structures commonly linked to social cognition. The literature mainly emphasized the role of the insula, ventromedial prefrontal cortex, orbitofrontal cortex, cingulate cortex, and amygdala in conjunction with brain networks comprising these areas, such as the somatic marker circuitry and the social cognition network. Other brain regions, including the dorsolateral prefrontal cortex, cuneus, precuneus, fusiform gyrus, superior temporal gyrus, cerebellum, parahippocampal gyrus, inferior frontal gyrus, frontopolar gyrus, superior parietal lobule, and superior longitudinal fasciculus (SLF) were also frequently mentioned. However, further research is needed to clarify the roles of some of these regions in EI. Limitations and future lines of research are discussed.

PMID:41527564 | PMC:PMC12790782 | DOI:10.7717/peerj.20539

Brain Behav. 2026 Jan;16(1):e71153. doi: 10.1002/brb3.71153.

ABSTRACT

PURPOSE: This study investigated the behavioral and resting-state neural correlates of clinical decision-making among expert gastroenterologists and novice medical students, aiming to understand how diagnostic expertise is reflected in either pre-task and/or post-task brain activity.

METHOD: Participants completed a clinical decision-making task while behavioral measures (accuracy and response time) were recorded. Resting-state fMRI data were acquired immediately before and following the task. Group differences in brain connectivity were analyzed using seed-based connectivity and multivariate partial least squares (PLS) analyses, focusing on the frontopolar prefrontal cortex (FPPFC) and its associated networks.

FINDING: Experts outperformed novices in diagnostic accuracy and speed, especially on "easy" cases, suggesting enhanced cognitive efficiency. Experts also showed more pronounced response time variation with task difficulty, potentially reflecting strategic modulation. Resting-state fMRI revealed that experts had increased post-task connectivity between the FPPFC and the paracingulate gyrus (PaCG), a brain area associated with the executive control network. Novices, by contrast, showed stronger FPPFC connectivity with the posterior cingulate cortex (PCC), part of the default mode network (DMN), indicating a return to internally directed cognition. PLS analyses further revealed that experts engaged executive and attentional network regions post-task, while novices primarily activated DMN regions. Notably, for the expert group only, increased brain activity in attention-related regions was associated with gastroenterologists who had slower, deliberate responses on easy cases.

CONCLUSION: Clinical expertise is associated with sustained engagement of goal-directed neural networks after task completion, potentially reflecting ongoing cognitive evaluation or preparation. In contrast, novices appear to disengage more readily, reverting to self-referential thought. These findings highlight distinct neural mechanisms that may support the development of diagnostic expertise.

PMID:41527524 | DOI:10.1002/brb3.71153

Neuroimage. 2026 Jan 10:121713. doi: 10.1016/j.neuroimage.2026.121713. Online ahead of print.

ABSTRACT

BACKGROUND: Sleep deprivation (SD) produces profound cognitive deficits, yet its integrative effects on large-scale cortical organization, metabolic alterations, and cognitive function remain unclear.

METHODS: Based on meta-analytic seeds, lesion network mapping was applied to connectome data from 1000 healthy individuals to generate an SD-related cortical weight map (SD-CWM). Thirty participants then completed a 24-hour acute SD protocol, during which resting-state and memory-task-evoked brain activities, untargeted metabolomics, and memory performance were measured under both rested wakefulness (RW) and SD.

RESULTS: (1) After SD, alterations in both resting-state and task-evoked activities within the SD-CWM were associated with memory performance decline, with resting-state changes showing stronger associations. (2) The SD-CWM was predominantly localized to the occipital cortex and encompassed the visual and ventral attention networks, showing spatial correspondence with nine neurotransmitter receptors that also tracked individual differences in resting-state changes. (3) Granger causality analysis indicated cortex-subcortex directional influences between the SD-CWM and subcortical nuclei under RW, whereas SD exhibited a shift toward subcortex-cortex patterns, centered on the caudal temporal thalamus. (4) Four metabolites were associated with resting-state changes within the SD-CWM, with cortical activity alterations showing stronger associations with metabolic variation than subcortical activity.

CONCLUSIONS: This study identifies an SD-related cortical map and characterizes its functional, directional, and metabolic associations, offering a systems-level perspective on the neural vulnerability underlying cognitive impairment after sleep loss.

PMID:41525894 | DOI:10.1016/j.neuroimage.2026.121713

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Jan 10:S2451-9022(26)00002-9. doi: 10.1016/j.bpsc.2025.12.013. Online ahead of print.

ABSTRACT

BACKGROUND: Differences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Our research identified five sensory processing 'phenotypes' with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity.

METHODS: We analyzed data from 146 autistic participants from the Province of Ontario Neurodevelopmental Network. We classified participants into five sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, and clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes.

RESULTS: Our sample was clustered into five sensory phenotypes. The machine learning models distinguished seven of the ten total pairs of sensory phenotypes using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas was predictive of sensory phenotype.

CONCLUSIONS: Sensory phenotypes in autism correspond to differences in functional connectivity across cortical, subcortical, and network levels. These findings support the view that variability in sensory processing is reflected in measurable neural patterns and motivate continued work to refine models of sensory processing, with the goal of better understanding and capturing the heterogeneity implicit in autism.

PMID:41525855 | DOI:10.1016/j.bpsc.2025.12.013

Brain Commun. 2025 Dec 23;8(1):fcaf495. doi: 10.1093/braincomms/fcaf495. eCollection 2026.

ABSTRACT

In Disorders of Consciousness, 18F-fluorodeoxyglucose PET (FDG-PET) is known to be effective in distinguishing vegetative state/unresponsive wakefulness syndrome from minimally conscious state, and when combined with MRI techniques, the risk of misdiagnosis decreases. However, FDG-PET studies on chronic patients with different etiologies (traumatic, vascular, and anoxic brain injury) are limited, and the association between metabolic activity and resting-state functional MRI (fMRI) networks remains unclear. This study combined FDG-PET with resting-state functional MRI and MRI to assess: i) the diagnostic accuracy of FDG-PET metabolism in different etiological groups of patients; ii) whether resting-state fMRI networks presence or absence was associated with higher versus lower FDG-PET metabolism. A group of 84 chronic patients underwent FDG-PET (47 vegetative state/unresponsive wakefulness syndrome, 31 minimally conscious state, and six emerged from a minimally conscious state), equally distributed in traumatic, vascular, and anoxic etiologies. Eight cases of covert cortical processing were identified. A subgroup of 68 patients also underwent resting-state fMRI. Standardized uptake values were calculated for these areas of interest: 10 resting-state fMRI networks, the precuneus, and a whole-brain mask. Patients in a vegetative state/unresponsive wakefulness syndrome exhibited a significant decrease in metabolism compared to patients in a minimally conscious state across all areas of interest. Patients with covert cortical processing showed intermediate metabolic levels between the two diagnostic categories. The anoxic group displayed a severe decrease in metabolism compared to patients with traumatic and vascular etiologies. The highest diagnostic accuracy among the areas of interest was reached in the precuneus and medial visual network (Area Under the Curve, AUC = 0.82-0.83). However, when anoxic patients were excluded, the diagnostic accuracy did not reach statistical significance, although the medial visual network and precuneus retained a trend of gradually increasing metabolism as clinical conditions improved. Identification of resting-state functional MRI networks was associated with increased metabolism in all networks at the group level, even excluding patients with severe structural damage. FDG-PET proves to be a technique capable of distinguishing vegetative state/unresponsive wakefulness syndrome from minimally conscious state even in chronic patients, although its diagnostic accuracy can be significantly affected by the etiology. There is a concordance between the metabolism level and the presence of resting-state fMRI networks.

PMID:41523181 | PMC:PMC12782017 | DOI:10.1093/braincomms/fcaf495

IEEE Access. 2025;13:172259-172278. doi: 10.1109/access.2025.3616256. Epub 2025 Sep 30.

ABSTRACT

Using temporal analysis of fMRI (functional Magnetic Resonance Imaging) data, we can characterize dynamic changes in brain connectivity over time. However, dynamic temporal analysis of fMRI data is challenging due to the high dimensionality of the datasets. Another fundamental challenge of dynamic temporal analysis of fMRI is the presence of non-neural artifacts that add sources of variation in the data that are not directly related to brain activity. For example, when data are acquired at different scanners at different temporal sampling rates and later analyzed as a single dataset, we have to contend with different number of image snapshots for different subjects. Also, high-frequency scans lead to more fine-grained temporal snapshotting than low-frequency scans. These factors can obscure true neural signals and lead to inconsistent characterization of dynamic brain connectivity across scans. Existing graph-based solutions often struggle with parameter sensitivity, since their outcomes depend heavily on selecting an arbitrary correlation threshold for defining network edges. In contrast, topological data analysis (TDA) sweeps across all threshold values to track the persistence of connectivity features, making it more robust for capturing fine-grained temporal dynamics. Clustering methods become imperative in this context as they offer a powerful means to uncover underlying structures within the high-dimensional temporal data. We address these challenges by developing a topological data analysis based temporal clustering pipeline targeted for dynamic functional connectivity derived from fMRI datasets that can preserve the dynamics of the temporal datasets and mask out the non-neural variability induced by varying sampling rates. The TDA-based pipeline extracts robust features that are invariant to non-neural noise and uses them to perform temporal clustering. We evaluate our framework by performing temporal clustering of resting-state fMRI-derived dynamic functional connectivity brain networks obtained from 316 subjects, each of whom was scanned thrice using different temporal sampling periods. The efficacy of our TDA-based pipeline is compared against three alternative approaches: direct time-series clustering, PCA-based dimensionality reduction and clustering, and a traditional fully connected network analysis pipeline with MDS-based dimensionality reduction. Additionally, we demonstrate that for a majority of cases, the number of clusters remains consistent for the same subjects scanned at different temporal sampling rates- showcasing the greater robustness of our TDA-based pipeline compared to other pipelines. The TDA pipeline achieved higher overlaps (59 %) in optimal cluster numbers across sampling cohorts, as well as higher pairwise similarity (74-77 %) between subjects' cluster solutions. This indicates that incorporating network topology via TDA enables more robust clustering of temporal fMRI datasets despite changes in sampling rates.Furthermore, we validate our method on a clinical dataset (ADHD-200). The TDA-based pipeline successfully captures consistent clustering patterns across different sites and scanning protocols, with higher stability of cluster assignments (> 80% similarity) and better separation of subject-level dynamics compared to existing approaches. This reinforces the method's robustness in multisite, multi-condition settings. Our results demonstrate that incorporating network topology via TDA significantly enhances the reliability of temporal clustering in fMRI studies, offering a robust framework for studying brain dynamics across heterogeneous acquisition settings.

PMID:41522212 | PMC:PMC12788382 | DOI:10.1109/access.2025.3616256

Alzheimers Dement. 2025 Dec;21 Suppl 2:e107257. doi: 10.1002/alz70856_107257.

ABSTRACT

BACKGROUND: Brainstem nuclei such as the locus coeruleus (LC) are amongst the earliest regions affected by tau pathology in Alzheimer's disease (AD). The LC's extensive noradrenergic projections shape brain network architecture and its structural integrity is associated with resilience against cognitive decline. However, the role of LC network connectivity in cognitive resilience remains unclear, as does its potential differential impact across distinct population subgroups who might specifically benefit from its augmentation.

METHODS: We included 393 cognitively unimpaired Aβ+ individuals (centiloid > 19) from the A4 study who underwent baseline resting-state fMRI and florbetapir Aβ-PET as well as longitudinal Preclinical Alzheimer's Cognitive Composite (PACC) assessment. Pearsons's correlation coefficient was used to create maps of LC functional connectivity (LC-FC) to 454 parcels of the Schaefer-Tian parcellation which were harmonised across scanners using NeuroCombat. Mixed-effects models with natural cubic splines (2 DOF) were used to test whether global, Yeo17 network, and individual parcel level LC-FC moderated PACC decline over time as well as in interaction with centiloid, sex, and APOΕ4 status, controlling for age, education, framewise displacement, treatment, and sex (when not interacted). Analyses were deemed significant following Benjamini-Hochberg false-discovery rate correction for multiple comparisons.

RESULTS: The LC showed widespread connectivity that was strongest within the limbic regions, hippocampus, and thalamus (Figure 1a). Greater global LC-FC was associated with attenuated cognitive decline (Figure 1b, p = 0.014), especially at higher levels of Aβ (Figure 1c, p <0.001). At the network level, LC-FC resilience was predominantly related to task-positive (control and dorsal/ventral attentional) networks, whilst in the default mode network greater LC-FC was associated with reduced cognitive decline at lower Aβ (Figure 1b/c). The effect of LC-FC on PACC decline was particularly pronounced in females at high Aβ levels (Figure 2b) and APOE-e4 carriers (Figure 2c), demonstrating a synergistic effect of sex and genetic risk on LC-mediated cognitive resilience (Figure 2d).

CONCLUSIONS: Our findings further support the role of the LC in cognitive resilience and suggest this particularly manifests from modulation of task-positive attentional and frontoparietal control networks. Moreover, female e4-carriers exhibit more pronounced attenuation of cognitive decline, suggesting they may especially benefit from augmentation of noradrenergic function.

PMID:41521485 | DOI:10.1002/alz70856_107257

Sci Rep. 2026 Jan 11. doi: 10.1038/s41598-026-35140-1. Online ahead of print.

ABSTRACT

Brain tumors, particularly meningiomas and gliomas, can profoundly affect neural function, yet their impact on brain dynamics remains incompletely understood. This study investigates alterations in normal brain function among meningioma and glioma patients by assessing dynamical complexity through the Intrinsic Ignition Framework. We analyzed resting-state fMRI data from 34 participants to quantify brain dynamics using intrinsic ignition and metastability metrics. Our results revealed distinct patterns of disruption: glioma patients showed significant reductions in both metrics compared to controls, indicating widespread network disturbances. In contrast, meningioma patients exhibited significant changes predominantly in regions with substantial tumor involvement. Resting-state network analysis demonstrated strong metastability and metastability/ignition correlations between regions in controls, which were slightly weakened in meningioma patients and severely disrupted in glioma patients. These findings highlight the differential impacts of gliomas and meningiomas on brain function, offering insights into their distinct pathophysiological mechanisms. Furthermore, these results show that brain dynamics metrics can be effective biomarkers for identifying disruptions in brain information transmission caused by tumors.

PMID:41521239 | DOI:10.1038/s41598-026-35140-1

Magn Reson Imaging. 2026 Jan 9:110611. doi: 10.1016/j.mri.2026.110611. Online ahead of print.

ABSTRACT

The aim of this study was to examine the effect of 2-h glucose infusion after an overnight fast on relative resting-state fMRI signal fluctuations. Our hypothesis was that neuronal glucose metabolism would be affected, and that this would lead to changes in signal power at specific frequency bands. Resting state fMRI (rsfMRI) scans were acquired in 9 subjects pre- and post-glucose ~2 h infusion. Fourier spectral analysis was performed using signals localized in the prefrontal cortex. Power spectra were calculated and statistical analysis was performed targeting the full spectral range using both a cluster-based and Wilcoxon signed rank test. For both tests, post-infusion signal power was significantly higher between the 0.015-0.1 Hz range. Moreover we observed differences at higher frequencies, normally attributed to cardiac or respiratory related signals. Our findings suggest that prolonged glucose infusion post-fasting may significantly modulate rsfMRI signal fluctuations in the prefrontal cortex that may be related to metabolic responses to glucose infusion. Future studies should investigate the dynamic effects of sustained glucose infusion using continuous fMRI, exploring implications for brain function and metabolic disorders like diabetes.

PMID:41520930 | DOI:10.1016/j.mri.2026.110611

Brain Res Bull. 2026 Jan 9:111724. doi: 10.1016/j.brainresbull.2026.111724. Online ahead of print.

ABSTRACT

Neurocognitive impairment, a prevalent complication in cirrhosis, correlates with disruptions in static functional connectivity(FC) of the brain. This study aims to explore altered spatiotemporal properties of blood oxygen-level dependent(BOLD) signals and elucidate their association with neurocognitive changes in cirrhotic patients. Between March 2023 and February 2025, we recruited 77 cirrhotic patients and 66 healthy controls(HCs) accompanied by resting-state functional magnetic resonance imaging(rs-fMRI) acquisition. Neurocognitive function was evaluated by psychometric hepatic encephalopathy score(PHES). The hidden Markov model(HMM) was used to explain variations in rs-fMRI averaged functional activity(AFA) and FC across the whole-brain by using a set of 5 unique recurring states. This study assessed the stability between different groups by comparing the time spent in each state and the mean duration of visits to each state. MannWhitney U tests, Kruskal Wallis H test, chi-squared test, correlation analysis. After Bonferroni correction, cirrhotic patients with minimal hepatic encephalopathy(MHE) showed significantly higher temporal stability and proportional time spent in state#1 characterized by lower AFA of frontoparietal network(FPN) and subcortical network and higher AFA of visual network (VN), as well as have lower fractional occupancy(FO) and averaged lifetime(ALT) in state#5, which is characterized by weaker within-network FC and lower AFA of limbic network(LN), salience network(SN), somatosensory motor network(SMN), and default mode network(DMN). We observed a significant positive correlation(Bonferroni corrected) between dynamic properties in state#5 and PHES performance. These findings suggest that disrupted brain functional synchrony across time is present in MHE and is linked to cognitive impairment.

PMID:41520895 | DOI:10.1016/j.brainresbull.2026.111724

Neurochem Int. 2026 Jan 9:106110. doi: 10.1016/j.neuint.2026.106110. Online ahead of print.

ABSTRACT

OBJECTIVE: This study investigated the neurofunctional effects of frontotemporal dual-site repetitive transcranial magnetic stimulation (rTMS) in patients with subjective tinnitus (ST).

METHODS: Ninety ST patients were randomly assigned to active (n=45) or sham (n=45) with rTMS. Fifty-two healthy subjects served as controls. All underwent resting-state fMRI (rs-fMRI) and clinical assessment (Tinnitus Handicap Inventory, THI) before and after a two-week intervention. Brain metrics included regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), degree centrality (DC), functional connectivity (FC), and structural covariance networks (SCN).

RESULTS: Active rTMS significantly reduced THI scores (P < 0.001). Rs-fMRI showed decreased ReHo in the right inferior parietal lobule, decreased fALFF in the right superior temporal gyrus (STG), but increased fALFF in the right temporal pole, and reduced DC in the right middle temporal gyrus (MTG) (all P < 0.05). FC weakened between right STG-MTG and right MTG-occipital gyrus (P < 0.05). SCN nodal centrality changed in right STG and left MTG (P < 0.05). No such changes were seen in sham or control groups (all P > 0.05).

CONCLUSION: Frontotemporal dual-site rTMS alleviates tinnitus, likely by modulating activity and connectivity in auditory and cross-modal integration regions, involving the default mode and auditory-visual processing networks.

PMID:41520884 | DOI:10.1016/j.neuint.2026.106110

Neuroscience. 2026 Jan 9:S0306-4522(26)00017-5. doi: 10.1016/j.neuroscience.2026.01.011. Online ahead of print.

ABSTRACT

Acute pain processing emerges from complex interactions among multiple brain regions, with local ion channels critically shaping neuronal communication. To better understand the role of HCN4 channels during acute pain in mice, a genetic brain-specific HCN4-KO was compared with pharmacological inhibition by the selective HCN4 channel blocker EC18. Stimulus-driven BOLD-fMRI measurements using graded peripheral thermal stimulation allowed brain-wide investigation of both discriminative and suppressive processes within ascending and descending pain pathways. Classical BOLD parameters and graph-theoretical analyses revealed that compared to controls, HCN4-KO showed a significant increase in brain activity in regions responsible for discriminative tasks, emotional pain processing and pain suppression including sensory cortex, amygdala and hypothalamus across both high and low thermal stimulation intensities. In striking contrast, acute inhibition of HCN4 with EC18 decreased activity in these same regions compared with both KO and control mice. Furthermore, comparing pre- and post-stimulation resting-state measurements revealed that HCN4-KO and controls exhibited a stimulation-induced increase in functional connectivity, whereas EC18-treated mice demonstrated a connectivity decrease. Taken together, genetic loss of HCN4 produced a hypersensitive phenotype in thermal pain processing, whereas acute pharmacological inhibition of the channel elicited an opposing hyposensitive phenotype.

PMID:41520869 | DOI:10.1016/j.neuroscience.2026.01.011

Alzheimers Dement. 2025 Dec;21 Suppl 2:e107430. doi: 10.1002/alz70856_107430.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous condition both in terms of the distribution of pathology deposition and clinical manifestation. The organization of functional brain networks is related to AD pathology, with recent work showing that higher dementia severity is related to the desegregation of brain networks (Zhang et al., 2023). However, the spatiotemporal heterogeneity of these changes in network segregation and how they contribute to different cognitive deficit profiles remains an open area of research. To contribute to this question, we applied a clustering-based data-driven disease progression model to system-level measures of network organization.

METHOD: We included 754 resting-state functional magnetic resonance imaging (fMRI) scans from cognitively impaired individuals (CDR > 0) enrolled in the Alzheimer's Disease Neuroimaging Initiative. The correlations of fMRI resting-state time series between nodes were used to construct brain networks (Chan et al., 2014). Nodes were assigned to a functional system based on a previously defined functional atlas (Power et al., 2011). Network segregation was calculated for each brain system as measures of system-level organization. The SuStaIn algorithm was used to simultaneously stage and subtype subjects according to their patterns of network segregation (Young et al., 2018). Functional systems that were clustered together were further analyzed collectively using linear regression models.

RESULT: SuStaIn identified two subtypes of alterations in system-level network segregation, which were upheld in k-fold cross validation. One cluster showed decreases in the segregation of sensory-motor systems and several additional systems, including the superior temporal gyrus, salience, and medial temporal parietal systems. The other cluster showed decreases primarily in segregation of association systems including the default mode network, task control systems, attention systems, and memory retrieval systems. Further, we observed a significant interaction effect between system-type and model-estimated disease stage and subtype.

CONCLUSION: Resting-state fMRI signals can be used to identify dissociable patterns of AD-related brain network desegregation relating to different profiles of brain network dysfunction and cognitive impairment. These findings begin to describe the spatiotemporal heterogeneity in brain network decline, and their relation to cognitive trajectories, which is relevant not only to AD prognostics but also evaluating clinical trial outcomes in cognitively-diverse patient populations.

PMID:41520286 | DOI:10.1002/alz70856_107430

Brain Stimul. 2026 Jan 8:103025. doi: 10.1016/j.brs.2026.103025. Online ahead of print.

ABSTRACT

BACKGROUND: Tremor in Parkinson's disease (PD) is a disabling symptom that often persists despite pharmacological treatment. High-intensity focused ultrasound (HIFU) targeting the ventral intermediate nucleus (VIM) alleviates Essential Tremor, but recent evidence suggests the zona incerta (ZI) may be a superior target for Parkinsonian tremor. This study compared the effects of transcranial ultrasound stimulation (TUS) to the VIM and ZI on postural and rest tremor, and examined related neural correlates using resting-state fMRI (rs-fMRI).

METHODS: In this within-subject, crossover study, 19 participants with PD and right-hand tremor received both left VIM- and ZI-TUS on the same day in randomized order, separated by a four-hour washout period. Tremor severity and rs-fMRI data were collected before and after each session. Normalized changes in tremor intensity, resting-state functional connectivity (Δrs-FC), and fractional amplitude of low-frequency fluctuations (ΔfALFF) within the cerebello-thalamo-cortical network were analysed.

RESULTS: TUS effects differed by target and tremor type. VIM-TUS significantly reduced postural tremor (p < 0.001) but not rest tremor, whereas ZI-TUS improved both postural (p = 0.005) and rest (p = 0.005) tremor. Although no overall group-level rs-FC changes were observed, individual Δrs-FC of the ZI following ZI-TUS correlated with tremor improvement (postural: r = 0.762, p < 0.001; rest: r = 0.586, p = 0.008), with similar findings for ΔfALFF.

CONCLUSION: ZI-TUS modulates tremor more robustly than VIM-TUS, suggesting that ZI may be a promising target for treatment of Parkinsonian tremor.

PMID:41519465 | DOI:10.1016/j.brs.2026.103025

Brain Res Bull. 2026 Jan 8:111712. doi: 10.1016/j.brainresbull.2025.111712. Online ahead of print.

ABSTRACT

Sleep deprivation (SD) impairs mood and cognition, yet its dynamic neural mechanisms remain unclear. Forty healthy adults (30-h SD) completed mood assessments, resting-state fMRI (rs-fMRI), and psychomotor vigilance task (PVT) tests at baseline and post-SD. Using dynamic functional connectivity (dFC) with sliding windows and k-means clustering, we identified two recurrent whole-brain states: (i) an economical state with sparse, weaker global coupling and (ii) a maladaptive compensatory state with globally strengthened synchronization. SD increased both the fraction of windows and mean dwell time (MDT) of the maladaptive state. Across participants, PVT lapses correlated positively with the maladaptive state's MDT and fraction of windows and negatively with those of the economical state. Finally, we built an interpretable predictive model of PVT lapses using competitive adaptive reweighted sampling partial least-squares regression (CARS-PLSR), which highlighted connections within the dorsal attention network (DAN) as key predictors. These findings link behavioral impairment to altered brain-state dynamics and provide a sparse, testable feature set that can support early risk stratification and intervention for SD-related cognitive decline.

PMID:41519179 | DOI:10.1016/j.brainresbull.2025.111712

Neuroimage Clin. 2026 Jan 6;49:103945. doi: 10.1016/j.nicl.2026.103945. Online ahead of print.

ABSTRACT

OBJECTIVE: Accumulating evidence highlights both structural and functional brain alterations in juvenile myoclonic epilepsy (JME), yet how these structural changes within white matter pathways drive functional disorganization remains largely unknown. Here, we aim to investigate white matter structure-function coupling (SFC) in treatment-naïve, newly diagnosed JME.

METHODS: Forty-seven patients with JME and 40 demographically matched healthy controls underwent diffusion-weighted imaging (DWI) and resting-state functional magnetic resonance imaging (fMRI). Tract-wise SFC was assessed using a multivariate linear regression, with the amplitude of low-frequency fluctuations as the dependent variable and four microstructural metrics-fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)-as independent variables. A support vector regression with five-fold cross-validation was employed to establish the associations with clinical severity.

RESULTS: JME patients exhibited widespread white matter microstructural alterations, including increased FA and decreased diffusivity metrics, alongside functional hyperactivity in multiple tracts. Notably, a significant reduction of SFC was observed in the left corticospinal tract (P = 0.008) and left inferior longitudinal fasciculus (P = 0.006). In addition, multimodal models combining structural, functional, and coupling metrics demonstrated superior predictive performance for clinical severity compared to single-modal analyses (P = 0.026).

CONCLUSION: These findings highlight white matter structure-function decoupling in the early stages of JME, specifically in key pathways relevant to motor and cognitive dysfunctions. Furthermore, the tract-specific SFC investigation offers a useful way for diagnosis, prognosis, and guiding personalized treatment strategies in this complex epilepsy syndrome.

PMID:41519070 | DOI:10.1016/j.nicl.2026.103945

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