Most recent paper

bioRxiv [Preprint]. 2026 Jan 9:2026.01.08.694967. doi: 10.64898/2026.01.08.694967.

ABSTRACT

Functional MRI-based graph theory has provided profound insights into the brain's functional organization, yet the neuroenergetic meaning of widely used graph-theoretical metrics remains poorly understood. Although resting-state research suggests a positive coupling between network topology and glucose metabolism, it remains unclear whether this relationship reflects a general principle of brain organization or a state-specific phenomenon. Here, we test the neuroenergetic interpretability of nodal graph-theoretical metrics by linking complex network topology to cerebral glucose consumption across diverse brain states. Leveraging simultaneous functional PET-MRI, we directly compare state-dependent fluctuations in glucose consumption and network topology during sensory, cognitive, and arousal conditions. We further assess metabolic-topological couplings in disease through a meta-analysis of resting-state FDG-PET and fMRI studies involving Alzheimer's disease, Parkinson's disease, major depressive disorder, and schizophrenia. Our results show that nodal graph-theoretical metrics exhibit state- and network-dependent metabolic associations, with coupling patterns diverging across experimental and disease contexts. Notably, frontoparietal and attentional networks show more conserved metabolic-topological coupling than other large-scale networks across states. These findings underscore a dynamic, complex interplay between metabolic demand and complex network organization, highlighting the need for a nuanced interpretation of the energetic underpinnings of nodal graph-theoretical metrics in health and disease.

PMID:41542587 | PMC:PMC12803132 | DOI:10.64898/2026.01.08.694967

bioRxiv [Preprint]. 2026 Jan 5:2026.01.05.697753. doi: 10.64898/2026.01.05.697753.

ABSTRACT

Brain regions perform distinct computations, and their signals propagate through the whole-brain white matter network. Yet, mathematical models that describe this signal propagation via purely passive diffusion can predict a considerable amount of the observed functional connectivity between regions. This raises a critical question: if so much functional connectivity can be explained by a passive process, how can we isolate the active process? Here, we calculate in closed-form an estimate for such an active signal in functional MRI by spatially deconvolving the effect of passive signal spread over the brain's structural connectivity using a higher-order network diffusion (HONeD) model. Across 770 Human Connectome Project subjects, we show that the resulting HONeD-innovation (HONeD-in) signal 1) sparsifies functional connectivity while retaining a well-connected network, 2) remodels resting-state networks (RSNs), 3) mixes the unimodal--multimodal hierarchical organization of RSNs into a circle with no clear hierarchy, and 4) deblurs task-activation maps. Together, our results highlight HONeD deconvolution as a generalizable new way to study resting-state and task fMRI brain signals.

PMID:41542509 | PMC:PMC12803073 | DOI:10.64898/2026.01.05.697753

bioRxiv [Preprint]. 2026 Jan 9:2025.12.22.695992. doi: 10.64898/2025.12.22.695992.

ABSTRACT

Discrimination is a chronic stressor linked to adverse health outcomes, particularly in racial and ethnic minorities. Understanding associations between early discrimination and the brain in childhood may help identify mechanisms through which discrimination impacts future health. Data from 4512 children (ages 9-11) and a subsample of Black, Indigenous, and People of Color (BIPOC; N = 1567) from the Adolescent Brain and Cognitive Development (ABCD) Study® was used to create linear mixed-effects models that evaluated associations between Perceived Discrimination (PD) and amygdala resting-state fMRI connectivity (AC) to the salience network (SN), default mode network (DMN), and thalamus. PD was measured using the youth self-reported PD Scale. Results indicated that greater PD significantly predicted greater AC to the right thalamus in our full sample. In secondary analyses, environmental and behavioral factors were evaluated as potential moderators for associations significant at least at a trend level in both our full sample and BIPOC subsample. In our BIPOC subsample, traumatic events experienced moderated the relationship between PD and AC to the anterior cingulate cortex (ACC; SN), such that greater traumatic experiences predicted stronger positive associations between PD and this connection. Results suggest PD impacts neural connections in early life, highlighting the need to consider the impact of discrimination on risk for psychopathology.

PMID:41542413 | PMC:PMC12803270 | DOI:10.64898/2025.12.22.695992

Nat Ment Health. 2025 Nov;3(11):1407-1416. doi: 10.1038/s44220-025-00525-0. Epub 2025 Oct 20.

ABSTRACT

Rumination, or perseverative negative self-referential thinking, is a hallmark of depression. In adults, a dynamic resting-state fMRI model of trait rumination was recently identified through predictive modelling. In adolescents, a development period during which rumination and depression increase, the neurobiological correlates of ruminative thinking are less clear. In the current preregistered study, we examine dynamic connectivity correlates of self-reported rumination in the largest sample of adolescents to date (n = 443, containing clinical and non-clinical individuals). Notably, the adult model failed to generalize to our sample. In addition, linear models trained on default-mode network (DMN) connectivity, as well as whole-brain connectome models, failed to generalize to held-out data. In an exploratory random forest analysis, we found significant prediction performance of a model where increased variability between DMN-cerebellum, DMN-dorsal attention network, and DMN-DMN connections was nominally associated with higher rumination. However, the model did not generalize to an external sample with lower rumination scores and a distinct scanner protocol. Our findings illustrate the difficulty of characterizing the neurodevelopment of risk factors for depression.

PMID:41541224 | PMC:PMC12803745 | DOI:10.1038/s44220-025-00525-0

Imaging Neurosci (Camb). 2026 Jan 13;4:IMAG.a.1065. doi: 10.1162/IMAG.a.1065. eCollection 2026.

ABSTRACT

Recent advancements in connectome analyses have enabled more precise measurements of brain network integrity. Identifying neural measures that can operate as mechanisms of cognitive reserve is integral for the study of individual variability in age-related cognitive changes. In the present study, we tested the hypothesis that network resilience, or the network's ability to maintain functionality when facing internal or external perturbations that cause damage or error, can function as a cognitive reserve (CR) candidate, modifying the relationship between cognitive and brain changes in a lifespan cohort of cognitively healthy adults. One hundred cognitively healthy older adults from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (50-80 years) underwent resting-state fMRI and neuropsychological testing at baseline and 5-year follow-up. Using undirected weighted adjacency matrices created from the Schaefer et al. (2018) 400-parcellation atlas and 19 additional subcortical regions (419 nodes in total), whole-brain network resilience was assessed through a targeted attack approach, where nodes were sequentially removed by nodal strength and resilience defined as the iteration of the steepest slope in the largest connected component (LCC) decay. We observed that network resilience moderated the effect of cortical thickness (CT) changes on longitudinal changes in Fluid Reasoning performance, even after adjusting for baseline differences, demographic factors, and the initial LCC of the unlesioned matrix, indicating that individuals with greater resilience were less sensitive to the effect of cortical thickness changes on changes in cognition. These findings support the use of targeted attack as a measure of cognitive reserve, suggesting that higher network resilience may allow individuals with reduced brain integrity to better cope with structural loss and maintain cognitive function.

PMID:41541057 | PMC:PMC12801055 | DOI:10.1162/IMAG.a.1065

Appetite. 2026 Jan 13:108456. doi: 10.1016/j.appet.2026.108456. Online ahead of print.

ABSTRACT

Eating less in response to negative emotions, called emotional undereating (EUE), is common in children, but research on the etiology of these behaviors is in its infancy. 91 children (aged 9-12, 46 females) completed EUE subscale of Children Eating Behavior Questionnaire and underwent resting-state fMRI. Of these, 43 participants also completed arrow task and 78 were followed up one year later. Compared to children with low-EUE, those with high-EUE exhibited fewer errors but longer reaction times, indicating over-control and reduced flexibility. Additionally, children with high-EUE revealed decreased resting-state functional connectivity (rsFC) within the prefrontal cortex and altered connectivity of insula. Notably, the rsFC between the insula and the temporal lobe could mediate the relationship between EUE and arrow task performance and positively predicted the performance one year later. These findings identify a potential stable neural marker of impaired cognitive control in children with EUE and provide new insights into the neurobiological basis of emotional undereating in childhood.

PMID:41539532 | DOI:10.1016/j.appet.2026.108456

Bone. 2026 Jan 13:117774. doi: 10.1016/j.bone.2025.117774. Online ahead of print.

ABSTRACT

Electroacupuncture has demonstrated established efficacy in treating postmenopausal osteoporosis, yet the central mechanisms underlying its action via the brain-bone axis remain incompletely understood. This study employed multimodal resting-state functional magnetic resonance imaging to investigate neurofunctional changes induced by electroacupuncture in a rat model of postmenopausal osteoporosis. Twenty-four female Sprague-Dawley rats were randomly allocated to electroacupuncture, sham, and model (ovariectomized) groups. The electroacupuncture group received an 8-week intervention at acupoints GB30, GB34, and GB39. We assessed brain function through amplitude of low-frequency fluctuation, regional homogeneity, and region-of-interest functional connectivity, while simultaneously measuring serum bone turnover markers via enzyme-linked immunosorbent assay. Our results demonstrated that electroacupuncture significantly improved bone microstructure and reduced bone resorption marker levels. Neuroimaging revealed enhanced cerebellar neural activity which correlated negatively with bone resorption, alongside decreased neural synchronization in the entorhinal cortex. Furthermore, strengthened functional connectivity between entorhinal and visual cortices positively correlated with bone formation markers, while weakened somatosensory-cerebellar connectivity correlated with reduced bone resorption. Bayesian mediation analysis provided strong statistical evidence for the role of the entorhinal-visual pathway involvement in bone formation regulation and cerebellar mediation of bone resorption suppression. These findings systematically reveal the association between electroacupuncture-induced brain functional reorganization and bone metabolic improvements, offering new insights into the role of the brain-bone axis in osteoporosis management.

PMID:41539419 | DOI:10.1016/j.bone.2025.117774

Imaging Neurosci (Camb). 2026 Jan 12;4:IMAG.a.1092. doi: 10.1162/IMAG.a.1092. eCollection 2026.

ABSTRACT

To improve the clinical utility of resting-state fMRI (rs-fMRI), enhancing its interpretability is paramount. Establishing links with electrophysiological activities remains the benchmark for understanding the neuronal basis of rs-fMRI signals. Existing research, while informative, suffers from inconsistencies and a limited scope of rs-fMRI metrics (e.g., seed-based functional connectivity). Phenotypic variables like sex and age are suspected to obscure reliable fMRI-electroencephalography (EEG) associations. A major contributing factor to these inconsistencies may be the neglect of macrovascular correction in rs-fMRI metrics. Given that macrovascular contributions can inflate rs-fMRI connectivity and power, they may lead to misleading fMRI-EEG associations that do not reflect genuine neuronal underpinnings. In this study, we addressed this by applying macrovascular correction and performing a systematic, inter-participant analysis of multiple rs-fMRI and EEG metrics. Our key findings are: (1) macrovascular correction enhances the relationship between EEG and rs-fMRI metrics and improves model fit in many instances; (2) sex significantly modulates EEG-fMRI associations; and (3) EEG complexity is significantly associated with resting-state functional activity (RSFA). This research provides crucial insights into the interplay between rs-fMRI and EEG, ultimately improving the interpretability of rs-fMRI measurements and building upon our prior work linking fMRI and metabolism.

PMID:41537053 | PMC:PMC12797145 | DOI:10.1162/IMAG.a.1092

Imaging Neurosci (Camb). 2026 Jan 12;4:IMAG.a.1091. doi: 10.1162/IMAG.a.1091. eCollection 2026.

ABSTRACT

Understanding how the older adult brain sustains cognitive flexibility remains a central question in aging research. Here, we analyzed resting-state fMRI data from the population-based CamCAN database (N = 628; age 18-88) and applied structural balance theory to measure functional network energy, a graph-theoretical proxy of network flexibility. In line with the SENECA model, our findings highlight midlife as a critical transition period: network energy is redistributed along the sensory-transmodal hierarchy, shifting from higher-level networks (DMN-FPN) to lower-level networks (SMN, CON, Auditory, Visual, Language). This reorganization (i) helps preserve the global wiring economy across the lifespan, hinting at an allostatic mechanism (i.e., stability through change) regulated by anti-correlated dynamics; and (ii) may support embodied semantic strategies in older adulthood, leveraging more predictive processing to sustain cognitive flexibility at lower costs. Taken together, our study reframes healthy neurocognitive aging as an allostatic process and provides a reference for extending the SENECA model to metabolism and neuropathology.

PMID:41537050 | PMC:PMC12797148 | DOI:10.1162/IMAG.a.1091

Netw Neurosci. 2026 Jan 8;10(1):80-92. doi: 10.1162/NETN.a.504. eCollection 2026.

ABSTRACT

Decades of neuroimaging have revealed that the functional organization of the brain is roughly consistent across individuals, and at rest, it resembles group-level task-evoked networks. A fundamental assumption in the field is that the functional specialization of a brain region arises from its connections to the rest of the brain, but limitations in the amount of data that can be feasibly collected in a single individual leave open the following question: Is the association between task activation and connectivity consistent across the brain and many cognitive tasks? To answer this question, we fit ridge regression models to activation maps from 33 cognitive domains (generated with NeuroQuery) using resting-state functional connectivity data from the Human Connectome Project as the predictor. We examine how well functional connectivity fits activation and find that all regions and all cognitive domains have a very robust relationship between brain activity and connectivity. The tightest relationship exists for higher order, domain-general cognitive functions. These results support the claim that connectivity is a general organizational principle of brain function by comprehensively testing this relationship in a large sample of individuals for a broad range of cognitive domains and provide a reference for future studies engaging in individualized predictive models.

PMID:41536424 | PMC:PMC12798649 | DOI:10.1162/NETN.a.504

Psychiatry Res Neuroimaging. 2026 Jan 8;357:112139. doi: 10.1016/j.pscychresns.2026.112139. Online ahead of print.

ABSTRACT

BACKGROUND: Opioid use disorder (OUD) is characterized by intense cue-induced craving and high relapse risk. This longitudinal fMRI study investigated whether oral naltrexone (50mg/day) (oral-NTX) modulates neural and behavioral responses to opioid cues, as well as resting-state brain connectivity.

METHODS: Thirty male patients with moderate-severe OUD underwent fMRI during an opioid-versus-neutral image task at baseline and after 2 weeks of oral-NTX treatment. The DDQ and OCDUS were administered for craving assessment. Task fMRI data was analyzed with linear mixed-effects models (3dLME). Resting-state fMRI was analyzed for ROI-to-ROI functional connectivity changes in key craving-related regions.

RESULTS: Oral_NTX significantly reduced both DDQ and OCDUS scores (p<0.01). Task-based fMRI revealed significant reductions in cue-induced activation in the anterior cingulate cortex (ACC) and cerebellum (whole-brain p<0.05, cluster-corrected). ROI analyses confirmed pre-to-post decreases in ACC and cerebellar activation (t>3.5, p<0.05). Larger craving reductions correlated with greater left superior temporal deactivation (t≈1.97, p<0.05). Resting-state connectivity analysis showed significant attenuation of intrinsic functional coupling between ACC-insula, nucleus accumbens (NAc)-amygdala, and cerebellum-hippocampus (p<0.05). These decreases complement task findings, indicating widespread dampening of salience and reward network interactions following oral-NTX.

CONCLUSION: Oral-NTX reduces cue-driven activation in cortical and cerebellar regions while dampening resting-state connectivity within craving circuits.

PMID:41534246 | DOI:10.1016/j.pscychresns.2026.112139

Diabetes Obes Metab. 2026 Jan 14. doi: 10.1111/dom.70476. Online ahead of print.

ABSTRACT

AIMS: Obesity involves both metabolic and neural dysfunction, yet the temporal dynamics of brain connectivity remain unclear. This study applied dynamic functional network connectivity (dFNC) analysis to reveal time-varying brain network patterns in obesity.

MATERIALS AND METHODS: Eighty-three individuals with obesity and 40 normal-weight controls underwent resting-state functional magnetic resonance imaging. After preprocessing and group independent component analysis, dFNC was estimated using a sliding-window approach and clustered into distinct connectivity states. Temporal metrics (fraction time, dwell time and transitions) were compared between groups, and correlations with clinical characteristics were analysed.

RESULTS: Three recurring connectivity states were identified. Compared with controls, individuals with obesity showed enhanced coupling among the default mode, attention and visual networks, with reduced network flexibility-manifested as prolonged dwell time and fewer transitions. Uncontrolled eating correlated positively with time spent in maladaptive states, whereas cognitive restraint was negatively associated with participation in integrative states.

CONCLUSIONS: Obesity is characterised by state-dependent reorganisation of large-scale brain networks and diminished temporal flexibility. These dynamic connectivity alterations are closely related to eating behaviour and metabolic characteristics, suggesting that dFNC provides a valuable neuroimaging framework for understanding impaired self-regulation in obesity and for guiding future intervention studies.

PMID:41532333 | DOI:10.1111/dom.70476

Neuropsychopharmacology. 2026 Jan 13. doi: 10.1038/s41386-025-02318-6. Online ahead of print.

ABSTRACT

Dynamic resting-state brain activity provides insight into intrinsic neural function and holds promise for predicting individual responses to cognitive demands and pharmacological interventions. This research could ultimately guide medication selection, yet links between network dynamics and medication effects on cognitive function require further validation. Here, we examined whether dynamic activity of an attentional network at rest relates to task-evoked brain activation on the Multi Source Interference Task (MSIT) following administration of methylphenidate (20 mg) and haloperidol (2 mg), which have opposing effects on attention and catecholaminergic function. Fifty-nine healthy adults completed resting-state and task-based fMRI on three separate days on which they received methylphenidate, haloperidol, or placebo in a double-blind placebo-controlled design. Coactivation pattern analysis determined time spent in the dorsal attention network (DAN) under placebo at rest. Linear mixed-effects modeling assessing the relationship between MSIT task activation under drug and time spent in DAN at rest under placebo and MSIT task activation under drug identified a significant interaction in the dorsolateral prefrontal cortex (dlPFC; p < 0.001). Post-hoc analyses indicated that more time in the DAN at rest under placebo was associated with decreased MSIT dlPFC activation under methylphenidate and increased dlPFC activation under haloperidol. Findings demonstrate that resting dynamics of an attentional network are linked to task-related brain responses under different drug conditions within a region implicated in attentional control and sensitive to catecholaminergic variance. Resting-state dynamics may predict pharmacological modulation of goal-directed cognition, highlighting the potential clinical utility of resting-state dynamics in predicting medication response and supporting individualized treatment.

PMID:41530553 | DOI:10.1038/s41386-025-02318-6

J Affect Disord. 2026 Jan 11:121143. doi: 10.1016/j.jad.2025.121143. Online ahead of print.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition, and its underlying neural mechanisms remain poorly understood.

METHODS: In this study, we investigated neural patterns and mechanisms of ASD using a voxel-wise measure of dynamic functional stability-Kendall's concordance coefficient-derived from whole-brain dynamic functional connectivity in a large, multicenter fMRI dataset. Furthermore, we explored the relationship between brain activity and behavioral measures. Finally, we performed a whole-brain functional connectivity (FC) analysis using the above three significant clusters as seed points.

RESULTS: ASD showed altered dynamic functional stability in three regions, with increased stability in the left frontal pole and reduced stability in the right central opercular cortex and left postcentral gyrus. Left frontal pole stability was positively associated with ADOS communication scores, whereas left postcentral gyrus stability was negatively associated with stereotyped behaviors. Seed-based FC analyses revealed decreased FC between the left postcentral gyrus and widespread sensorimotor-parietal regions in ASD. Symptom-connectivity analyses further showed broad negative correlations between FC and ADOS scores: reduced frontal and sensorimotor connectivity was linked to more severe communication and social impairments.

CONCLUSION: Our study revealed abnormal temporal stability of functional brain activity in ASD, thereby enhancing our understanding of ASD pathogenesis. Moreover, this dynamic stability analysis may serve as a reliable tool for early autism diagnosis.

PMID:41529734 | DOI:10.1016/j.jad.2025.121143

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