Most recent paper

Psychiatry Res Neuroimaging. 2026 Sep 20;364:112338. doi: 10.1016/j.pscychresns.2026.112338. Online ahead of print.

ABSTRACT

The neurobiological basis of cognitive impairment in Binswanger disease remains unclear. We compared 21 Binswanger disease patients with mild cognitive impairment (BD-MCI) and 21 without cognitive impairment (BD-C) using resting-state fMRI. No healthy control group was included. All participants completed cognitive assessments prior to scanning. Graph-theoretical and dynamic functional connectivity (dFC) analyses were performed, with most comparisons corrected for multiple comparisons (P FDR < 0.05). Group differences were characterized by significantly reduced global local efficiency in BD-MCI, suggesting widespread network disintegration. Regionally, nodal alterations were observed across frontoparietal, temporal, and subcortical regions, with increased degree centrality and nodal efficiency in bilateral inferior parietal lobule and superior parietal gyrus, but decreased clustering and efficiency in putamen, pallidum, and Heschl's gyrus(Cohen's d range: 0.63-1.45). dFC analysis revealed altered state frequency, mean dwell time, and total transitions between groups (FDR-corrected P < 0.05; Cohen's d range: 0.64-1.19), indicating disrupted temporal coordination and reduced dynamic flexibility. Collectively, these findings suggest that BD-MCI exhibits aberrant topological reorganization, characterized by impaired local information processing and altered temporal dynamics, which may reflect a reduced capacity for adaptive network reconfiguration. These preliminary findings provide potential neural correlates for early cognitive decline in Binswanger disease.

PMID:42785052 | DOI:10.1016/j.pscychresns.2026.112338

Dev Cogn Neurosci. 2026 Sep 21;82:101823. doi: 10.1016/j.dcn.2026.101823. Online ahead of print.

ABSTRACT

Refinement of large-scale functional networks during adolescence facilitates maturation of cognitive and affective functions vital to adult wellbeing, including working memory and emotion regulation. Understanding how functional connectivity (FC) subserves these abilities can offer valuable insight into typical neurodevelopment and brain-based markers of vulnerability to psychopathology. The Adolescent Brain Cognitive Development (ABCD) Study includes a task designed to engage both working memory and affective processing: the Emotional N-back (EN-back). This study applied connectome-based predictive modelling (CPM) to baseline EN-back fMRI data (n = 4326) to examine whole-brain FC patterns underlying EN-back performance. Separate models were run to predict overall and stimulus-specific accuracy (i.e., places, happy, fearful, and neutral faces). To probe neurodevelopmental change, CPM was applied to N-back fMRI data among adults in the Human Connectome Project (HCP; n = 515). CPM identified networks predictive of overall task performance (Total-accuracy) and all stimuli (rho = 0.26 - 0.35, p < .001) in ABCD. Total-accuracy network strength was a significant predictor of externalizing problems (β = -0.002, t = -5.06, p = 0.0000004), and generalized to predict task performance across brain states (resting-state: rho = 0.13, p < .001) and time (2-year follow-up: rho = 0.37, p < .005). CPM also identified a network predictive of Total N-back accuracy among adult HCP participants (rho = 0.48, p < .05), characterized by common and distinct FC patterns. This work provides novel insight into whole-brain mechanisms of EN-back performance at the onset of adolescence and patterns of neurodevelopment from early adolescence to adulthood.

PMID:42785005 | DOI:10.1016/j.dcn.2026.101823

Front Aging Neurosci. 2026 Sep 9;18:1893842. doi: 10.3389/fnagi.2026.1893842. eCollection 2026.

ABSTRACT

OBJECTIVE: Metabolic syndrome (MetS) may amplify neural vulnerability via vascular and inflammatory pathways. Existing literature suggests there are alterations to functional brain network organization in adults with MetS, though the specific patterns and their relation to cognitive vulnerabilities warrant further investigation, particularly in a midlife sample.

METHOD: Cognitively unimpaired, community-dwelling adults (N = 223; ages 40-65; MetS = 73) were included. Resting-state fMRI data were preprocessed with fMRIPrep and analyzed using the Schaefer 400-parcel atlas. Functional connectivity (FC) metrics included within- and between-network connectivity, system segregation, modularity, and global efficiency. Cognitive performance was measured via neuropsychological tests for memory and executive functioning. Generalized linear models (adjusted for age, sex, framewise displacement) were used to examine group differences in FC metrics and associations between FC metrics and cognitive performance.

RESULTS: MetS participants showed reduced within-network connectivity in the default mode network (DMN; adjusted p = 0.038, partial η 2 = 0.035). Follow-up component-level analyses indicated abdominal obesity and low HDL cholesterol were associated with reduced connectivity in DMN (abdominal obesity: adjusted p = 0.027, partial η 2 = 0.029; cholesterol: adjusted p = 0.002, partial η 2 = 0.058). Reduced DMN connectivity was associated with poorer verbal fluency (adjusted p = 0.026, partial R 2 = 0.037). Exploratory analyses identified age and sex as factors that interacted with MetS group to influence FC metrics (p's < 0.050, partial η 2's = 0.020-0.030).

CONCLUSION: Midlife MetS was associated with modest reductions in within-network DMN connectivity, with abdominal obesity and low HDL cholesterol as the primary contributing MetS components. Reduced DMN connectivity was associated with poorer verbal fluency performance. These findings suggest that subtle differences in higher-order functional brain network organization associated with metabolic syndrome can be detected during midlife and are related to cognitive functioning, highlighting the importance of prevention and intervention of cardiometabolic risk in midlife to preserve network integrity and cognitive health.

PMID:42780438 | PMC:PMC13598075 | DOI:10.3389/fnagi.2026.1893842

medRxiv [Preprint]. 2026 Sep 20:2026.09.18.26363360. doi: 10.64898/2026.09.18.26363360.

ABSTRACT

BACKGROUND: Low intensity focused ultrasound (FUS) offers a noninvasive method for directly modulating deep brain structures with millimeter-scale precision, potentially addressing a major limitation of existing noninvasive neuromodulation approaches. The amygdala is a key node in affective neurocircuitry and a compelling target for psychiatric intervention. We conducted a first in human active controlled trial ( NCT05147142 ) of amygdala-targeted FUS in patients with major depressive disorder, examining safety and target engagement.

METHODS: Ten participants with major depressive disorder completed a single-blind randomized crossover trial with blinded symptom ratings, comparing imaging guided FUS targeting the right amygdala versus left primary somatosensory cortex as an active control. FUS comprised two 10 minute applications within diagnostic ultrasound safety limits. Primary outcomes were safety and target engagement assessed with BOLD fMRI during sonication, post-sonication arterial spin labeling (ASL) and resting state functional connectivity. Secondary and exploratory outcomes included symptom change and imaging x symptom relationships.

RESULTS: Nine participants completed both sessions. No serious adverse events occurred; neurological, neuropsychological, and MRI safety assessments were unremarkable. Adverse events were more frequent after amygdala than control sonication (p=0.028); one participant experienced clinical worsening following amygdala sonication and required monitoring. Amygdala sonication produced greater perfusion change in the targeted amygdala compared to the control region (p<0.001), and ipsilateral hippocampus (p<0.001). BOLD fMRI during sonication demonstrated engagement of ventromedial prefrontal and rostral anterior cingulate regions, while post sonication resting state connectivity decreased between basolateral amygdala and sensorimotor regions (corrected ps<0.05). Symptom improvement did not differ between conditions, but exploratory imaging x symptom relationships were significant. Spontaneous reports of calm, clarity, or lightness occurred after amygdala but not control sonication (p<0.001).

CONCLUSIONS: Amygdala targeted FUS produced anatomically specific, multimodal evidence of neuromodulation relative to active control, with a manageable safety profile. Convergent target and circuit level signals support further development of FUS as a precision approach for modulating deep brain targets in psychiatric disorders.

KEY WORDS: low-intensity focused ultrasound, amygdala, neuroimaging, depression, anxiety.

PMID:42780377 | PMC:PMC13596687 | DOI:10.64898/2026.09.18.26363360

Res Sq [Preprint]. 2026 Sep 20:rs.3.rs-10895585. doi: 10.21203/rs.3.rs-10895585/v1.

ABSTRACT

Heart rate variability biofeedback (HRVB) is a non-pharmacological intervention that trains individuals to breathe at their cardiovascular resonance frequency to enhance autonomic regulation. While HRVB improves cardiovascular function across clinical conditions, the directional neural pathways through which it modulates the central autonomic network (CAN) remain poorly understood. Using a publicly available open-access dataset, we conducted a secondary analysis of effective (directional) neural connectivity (EC) data from 32 healthy participants randomized to an 8-week HRVB intervention (n = 15) or a video game Control intervention (n = 17). Pre- and post-intervention resting-state functional magnetic resonance imaging (fMRI) data were analyzed using spectral dynamic causal modeling (DCM) combined with parametric empirical Bayes to examine group differences in EC changes (ΔEC). We also evaluated whether connectivity changes correlated with changes in peripheral autonomic regulation, indexed by the standard deviation of normal-to-normal intervals (ΔSDNN). Compared with the Control group, HRVB showed strong evidence (posterior-probability = 1) of increased ΔEC from the medial prefrontal cortex to the anterior cingulate cortex and bilateral thalamus post-treatment, along with reduced ΔEC from the right hippocampus to the right ventrolateral prefrontal cortex (VLPFC). Furthermore, the regression coefficient linking hippocampus-to-VLPFC ΔEC with ΔSDNN was more (posterior-probability = 1) negative in the HRVB group than in Controls, indicating an intervention-dependent association between autonomic changes and effective connectivity. These findings indicate that HRVB alters directional fronto-limbic communication within the CAN, providing a circuit-level framework for understanding how resonance breathing modulates central autonomic control.

PMID:42780277 | PMC:PMC13596646 | DOI:10.21203/rs.3.rs-10895585/v1

bioRxiv [Preprint]. 2026 Sep 15:2026.09.09.750386. doi: 10.64898/2026.09.09.750386.

ABSTRACT

The biological heterogeneity of psychotic disorders (PDs) has motivated the identification of psychosis imaging neurosubtypes (PINs), which aims to stratify the PD population into subgroups with similar neurobiological underpinnings. Yet the influence of sex, although well documented in the literature, has not been well counted in the existing subtyping paradigm. Here, we introduce concurrent supervised-unsupervised representative subspace clustering (CSU-RSC), a general functional neurosubtyping framework that jointly estimates cluster-specific subspaces and a partition while softly incorporating a supervised label, aiming to identify sex-dominant subgroups based on differences in the spatial organization of brain networks. This soft supervising design differentiates CSU-RSC from unsupervised approaches that completely ignore a label as well as from supervised approaches that exclusively analyze each label group separately. On simulated data, CSU-RSC recovered the ground truth of subgroups more accurately than its unsupervised counterpart. Applied to resting-state fMRI from 1,239 individuals with psychosis, CSU-RSC identified two sex-dominant PINs that replicated across discovery and validation sets, and showed significant differences in clinical characteristics, including cognitive impairment and symptom severity, with the most cognitively impaired subtype being female-dominant. Projecting controls onto the patient-derived subspaces showed that the female-dominant pattern did not persist in controls, suggesting psychosis-specific sex heterogeneity. Overall, by incorporating sex as an explicit source of information, CSU-RSC highlights the value of sex for resolving biological heterogeneity and provides a basis for more reproducible and biologically informative patient stratification.

PMID:42780225 | PMC:PMC13596575 | DOI:10.64898/2026.09.09.750386

Front Neurosci. 2026 Sep 9;20:1910307. doi: 10.3389/fnins.2026.1910307. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic fatigue syndrome (CFS) is a complex disorder characterized by persistent fatigue, sleep disturbance, and cognitive impairment. It substantially impairs both physical and mental health. Tai Chi, a traditional Chinese mind-body exercise, may improve symptoms in patients with CFS. However, its neural mechanisms remain unclear. This study investigated the effects of Tai Chi on spontaneous brain activity in patients with CFS using resting-state functional magnetic resonance imaging (rs-fMRI) and amplitude of low-frequency fluctuations (ALFF).

METHODS: Sixty patients with CFS were randomly assigned in a 1:1 ratio to either a Tai Chi group or a health education group; 57 participants completed both fMRI scans and fatigue assessments and were included in the complete-case analysis. Both groups underwent a 12-week intervention consisting of three 60-min sessions per week. Adherence was monitored using weekly logs. Fatigue severity was assessed before and after the intervention using the Multidimensional Fatigue Inventory (MFI-20). Resting-state fMRI data were acquired at both time points. Preprocessing included motion correction, normalization to Montreal Neurological Institute space, and spatial smoothing. ALFF was calculated to quantify regional spontaneous neural activity. Correlation analyses were performed to assess associations between changes in ALFF and MFI-20 scores. Harms were monitored throughout the intervention period.

RESULTS: Fifty-seven participants were included in the complete-case analysis (Tai Chi, n = 29; health education, n = 28). Compared with health education, Tai Chi produced greater reductions in MFI-20 total score from baseline to week 12 (between-group change difference, -9.50 points; 95% CI, -12.31 to -6.70; Cohen's d = -1.83; P < 0.001). Greater improvements were also observed in overall fatigue, physical fatigue, mental fatigue, activity reduction, and interest reduction, with between-group differences ranging from -1.26 to -2.44 points. In imaging analyses, Tai Chi was associated with increased ALFF in prefrontal, hippocampal/striatal, and supplementary motor regions and decreased ALFF in occipital and temporal regions, using voxel-level P < 0.001 and cluster-level family-wise error correction. Exploratory brain-behavior analyses showed that increases in ALFF in the left medial superior frontal gyrus and left dorsolateral superior frontal gyrus were associated with greater reductions in MFI-20 scores, whereas decreases in ALFF in the left inferior occipital gyrus were associated with fatigue improvement. No falls, injuries, severe post-exertional malaise, or intervention-related medical events were reported through weekly participant logs or supervised-session monitoring.

CONCLUSION: A 12-week Tai Chi intervention was associated with greater fatigue improvement than health education and with region-specific changes in resting-state spontaneous brain activity in patients with CFS. Tai Chi was associated with reduced fatigue and altered spontaneous brain activity in prefrontal, hippocampal, motor, and sensory-related regions in patients with CFS. These findings suggest that Tai Chi may modulate fatigue-related brain function, which may contribute to improvements in fatigue severity. Future studies should replicate and extend existing network-level findings using prespecified, adequately powered connectivity and mediation analyses with active exercise comparators and longer-term follow-up.

CHINESE CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=223860, ChiCTR2400082268; March 26, 2024.

PMID:42780216 | PMC:PMC13597717 | DOI:10.3389/fnins.2026.1910307

bioRxiv [Preprint]. 2026 Sep 15:2026.09.09.750391. doi: 10.64898/2026.09.09.750391.

ABSTRACT

Iron is essential for neurophysiological health. Prior work has shown that subcortical tissue iron levels are the highest among all brain regions but also change over the lifespan. Recent functional MRI (fMRI) studies have provided evidence that an fMRI-derived iron-sensitive signal may index relative iron concentration in subcortical regions. In this context, we aimed to characterize the lifespan trajectory of this fMRI-derived iron-sensitive signal (relative iron quantification) in 10 subcortical regions and 14 thalamic nuclei from 881 healthy participants aged 12 to 88 years, using resting-state fMRI. Regressions were run with age as a predictor, separately for males (N = 429) and females (N = 452). The main results show non-linear effects of age, with an overall increase in iron levels across most subcortical regions within each sex. Six of the 14 thalamic nuclei in male participants had similar slopes of their lifespan iron levels, showing some homogeneity. The thalamic nuclei of female participants showed less agreement in slopes. This study provides new insight into the typical trajectory of iron levels across all major subcortical regions throughout the lifespan.

PMID:42779872 | PMC:PMC13596551 | DOI:10.64898/2026.09.09.750391

bioRxiv [Preprint]. 2026 Sep 14:2026.09.08.750194. doi: 10.64898/2026.09.08.750194.

ABSTRACT

The eigenvalue structure of resting-state fMRI (RS-fMRI) signals provides a compact representation of its variance-covariance structure, yet the information it encodes remains unclear. In this study, we introduce an eigenvalue-based framework to characterize RS-fMRI data using two features derived from the eigenspectrum: the log10-transformed first eigenvalue (log10(λ1)) and the slope of the log10-transformed spectrum (β). We systematically evaluated these features across 14 denoising strategies using two independent datasets (China167 (83 males, 84 females; mean age +/- SD: 41 +/- 14 years old) and HCP1200 (425 males, 501 females; mean age +/- SD: 29 +/- 4 years old)). Specifically, we used singular value decomposition to obtain the eigenspectrum of the cortical BOLD signals after preprocessing and nuisance regression, and a linear model to parameterize it. We then examined the relationships between the eigenvalue parameters, head motion, and functional connectivity metrics across subjects and denoising strategies. Our key findings include: (1) log10(λ1) and β strongly covaried with one another across subjects, denoising strategies, and datasets, indicating that they capture highly coherent aspects of the eigenspectrum; (2) both features were systematically influenced by denoising strategies; (3) within each denoising strategy, participants with greater log10(λ1) had greater mean framewise displacements (mFD), demonstrating sensitivity to residual motion effects, for all strategies in HCP1200 and 12/14 in China167; (4) across denoising strategies, mean log10(λ1) reflected the proportion of functional connectivity edges significantly associated with motion, indicating that higher log10(λ1) reflects more widespread motion-related contamination across large-scale functional networks; and (5) global signal regression consistently reduced log10(λ1), whereas spike regression had dataset-dependent effects. Together, these results establish eigenvalue signatures as robust and sensitive metrics for quantifying residual motion effects in RS-fMRI and provide a framework for evaluating denoising performance based on eigenvalue structure. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/EigenvalueSignature.

PMID:42779620 | PMC:PMC13596168 | DOI:10.64898/2026.09.08.750194

bioRxiv [Preprint]. 2026 Sep 15:2025.12.09.693219. doi: 10.64898/2025.12.09.693219.

ABSTRACT

Resting-state fMRI is routinely described in terms of a small set of recurring whole-brain states. Far less is known about what happens between them. Two pictures compete. In one, the brain toggles : it switches between qualitatively distinct regimes, and a transition is a discrete event. In the other, apparent states are convenient labels applied to a continuous trajectory, and transitions are mostly boundaries imposed by the analyst rather than events in the brain. We adjudicated between them with a multi-level Switching Linear Dynamical System fit to Human Connectome Project data ( N = 500; 254 cortical and subcortical regions), in which every participant has their own equations of motion and switching parameters, tied to group-level parameters and estimated jointly with them. The model estimates the dynamics on either side of a boundary, so transitions can be characterized rather than merely counted. Transitions were structured and strongly heterogeneous. Four of the eight transitions examined reversed the estimated flow between two samples acquired 720 ms apart, two of them deeply, while others merely reoriented and were "one-sided", with activity flat before the switch and changing only afterwards. Sharp and smooth transitions shared destination states and engaged overlapping networks, so the heterogeneity cannot be attributed simply to hemodynamic filtering. What predicted sharpness was the direction of travel, such that the brain drifts out of a dominant, long-dwell state regime and is sharply switched into it. To link these system-level dynamics to anatomy we introduce transition importance , which asks how much the model's evidence for a particular switch depends on a region's signal. The evidence accumulated over seconds before a switch, raising its odds by 1.8 to 3.2 times, and discharged immediately after. In several transitions the regions that steer a state shared no region with those that end it, so a region's activity magnitude does not determine its contribution to system-level dynamics. Our findings suggest that spontaneous brain activity is organized as much by how the brain moves between configurations as by which ones it occupies, making transitions themselves an important target of study.

PMID:42779576 | PMC:PMC13596141 | DOI:10.64898/2025.12.09.693219

Mov Disord. 2026 Sep 24. doi: 10.1002/mds.70512. Online ahead of print.

ABSTRACT

BACKGROUND: Paroxysmal kinesigenic dyskinesia (PKD) causes brief, movement-triggered dystonic or choreic attacks that can severely affect daily life. Though most patients eventually remit, timing and rate of improvement differ substantially across individuals, and predictive biomarkers remain lacking. Evidence indicates that PKD reflects distributed network dysfunction.

OBJECTIVE: To investigate how structural wiring supports or constrains these dysfunctions, as evaluated by structure-function (structural connectivity-functional connectivity [SC-FC]) coupling.

METHODS: Diffusion kurtosis imaging and resting-state functional magnetic resonance imaging (fMRI) were obtained in 95 patients with PKD and 44 healthy controls (HC). SC-FC coupling was quantified at nodal, intranetwork, and internetwork levels. Patients were classified as remission or nonremission. Machine-learning classifiers based on multilevel SC-FC features were trained to distinguish PKD from HCs and to predict remission status. Associations between SC-FC features and disease duration were assessed using Spearman's correlation.

RESULTS: Patients with PKD exhibited widespread SC-FC coupling abnormalities consistent with distributed rather than focal network dysfunction. Remission and nonremission subgroups exhibited distinct SC-FC signatures, centering on cerebellar, somatomotor, and default-mode systems. Machine-learning classifiers discriminated PKD patients from HCs (area under the receiver operating characteristic curve [AUC] = 0.91) and remission from nonremission patients (AUC = 0.95). Cerebellar-default-mode network coupling was the top discriminative feature and correlated positively with disease duration (Spearman's r = 0.26, P = 0.031, FDR corrected).

CONCLUSIONS: Multilevel SC-FC coupling analyses reveal systems-level abnormalities in PKD and suggest that partial normalization of coupling patterns accompanies remission. Cerebellar-default-mode network coupling may serve as a sensitive imaging biomarker of remission status and disease progression, highlighting coupling-based metrics as candidates for predicting PKD trajectory. © 2026 International Parkinson and Movement Disorder Society.

PMID:42779529 | DOI:10.1002/mds.70512

Brain Res Bull. 2026 Sep 23:112127. doi: 10.1016/j.brainresbull.2026.112127. Online ahead of print.

ABSTRACT

Living at high altitudes (HA) poses challenges to the brain due to hypobaric hypoxia. However, most neuroimaging studies have focused on group-level comparisons, leaving the brain features associated with individual differences in HA residence duration incompletely characterized. We therefore used a data-driven, individualized, multimodal machine learning framework for exploratory cross-validated association analysis to estimate individuals' HA residence duration after adjustment for age and gender. The analysis included 217 healthy migrants at HA and incorporated whole-brain anatomical morphometry, structural connectivity (SC), and functional connectivity (FC). Resting-state FC showed the highest estimate-target correlation among the unimodal models (r = 0.34, permutation p = 0.01, out-of-sample predictive R² = -0.15, MAE = 41.04 months, RMSE = 52.39 months). Combining FC and SC produced a numerically higher r and lower MAE and RMSE (r = 0.36, permutation p = 0.01, out-of-sample predictive R² = 0.00, MAE = 38.37 months, RMSE = 48.86 months) than FC alone, but the difference in correlation was not statistically significant. The FC model thus showed a statistically significant but modest cross-validated association with the residualized target, but its accuracy was insufficient to support clinical prediction or monitoring of HA residence duration. Meanwhile, FC within the somatomotor network (SMN) showed a positive contribution to model estimation, while FC between the SMN and the default mode network (DMN) showed a negative contribution. Positive contributions were observed for SC between the DMN and the frontoparietal network and for FC between the DMN and the dorsal attention network. SC between the thalamus and the posterior superior temporal sulcus (pSTS) showed a positive contribution to model estimation. Whether these residence-duration-associated connectivity patterns reflect within-person changes over time requires longitudinal investigation. As no behavioral validation was performed in this study, the functional significance of these connectivity patterns remains to be determined.

PMID:42778069 | DOI:10.1016/j.brainresbull.2026.112127

JMIR Res Protoc. 2026 Sep 23;15:e90667. doi: 10.2196/90667.

ABSTRACT

BACKGROUND: The transdiagnostic symptom of anhedonia, or the loss of pleasure in previously pleasurable things, is common across several chronic physical and mental illnesses. Music therapy has been effective in treating a range of conditions associated with anhedonia, but only 3 cases have been reported that targeted increasing hedonic capacity and all relied on descriptive data (qualitative and quantitative). Functional magnetic resonance imaging (fMRI) evidence demonstrates that pleasure is associated with increased activity in the limbic system, including the anterior cingulate cortex, as well as the prefrontal cortex. As such, changes within these regions may provide objective evidence to explore how music therapy acts to reduce anhedonia symptomatology.

OBJECTIVE: Our primary aim is to examine the feasibility of a music therapy intervention to increase hedonic capacity among individuals with anhedonia. We also aim to examine congruence across psychometric results and interview data to generate estimates of effect size and explore participants' qualitative experiences. In addition, we aim to identify any correspondence between these outcomes and changes in brain activity associated with reward processing during targeted fMRI tasks. This study will generate hypotheses for testing in future studies.

METHODS: This pilot study uses a sequential mixed methods design to first identify whether there are pre-post changes in the primary outcome measures and then to identify correlations between interview data, psychometric data, and objective measures of brain structure and functioning (fMRI). Participants were 13 adults with high cognitive function who scored within the clinical range for anhedonia on the Dimensional Anhedonia Rating Scale (DARS). Participants were engaged in a 4-month music therapy intervention developed through preliminary case studies, which involved daily independent pleasure-focused music activities and regular contact with a therapeutic guide. The fMRI data acquired include resting-state fMRI and task-based functional paradigms designed to probe musical pleasure (music listening task) and reward prediction (reversal reward learning task). Data will be analyzed using individual general linear models to identify associations with symptom improvement, as well as longitudinal changes in functioning.

RESULTS: The study received internal funding support from the University of Melbourne in September 2024. Data collection commenced in August 2025 and was concluded by mid-2026. As of September 2026, 13 participants have been recruited, and data will be analyzed by the end of 2026, with results expected to be published in 2027.

CONCLUSIONS: The results of this pilot study will contribute to knowledge about the effects of this music therapy intervention on reducing anhedonia and introduce a study protocol that could be used in subsequent, more robust studies to explore potential mechanisms.

PMID:42777149 | DOI:10.2196/90667

Brain Commun. 2026 Jul 16;8(4):fcag278. doi: 10.1093/braincomms/fcag278. eCollection 2026.

ABSTRACT

As one of the earliest pathological events in Alzheimer's disease, amyloid-beta accumulation has been linked to alterations in large-scale brain networks, especially the default mode network. While most prior work has focused on resting-state functional connectivity or task-evoked activation, how amyloid-beta relates to task-modulated connectivity that directly supports behaviour is less explored. Here, we examined the relationship between amyloid-beta burden and task-modulated connectivity during a Letter-Sternberg verbal working memory task in 84 cognitively normal older adults from an ongoing longitudinal cohort (aged 56-71 years; 41 females). To further understand these connectivity alterations, we tested whether these connections were associated with task performance or with cognitive reserve proxies. We found that higher amyloid-beta burden was associated with reduced connectivity-specifically during the probe-phase of working memory retrieval. At the regional scale, lower node strength in posterior-medial regions of the default mode network was related to worse task performance and mediated the amyloid-behaviour relationship. At the network scale, altered coupling between the default mode and dorsal attention networks was related to task performance but did not mediate the amyloid-behaviour relationship. Moreover, physical activity was associated with network-level coupling but not with regional node strength. These findings suggest that amyloid-beta burden disrupts retrieval-related working memory systems, particularly regions of the default mode network, while coupling between the default mode and dorsal attention networks is related to physical activity, a cognitive reserve proxy. Collectively, task-modulated connectivity provides insight into pathways through which early Alzheimer's disease pathology influences working memory performance and may inform targeted lifestyle or neuromodulation interventions for older adults.

PMID:42774984 | PMC:PMC13594969 | DOI:10.1093/braincomms/fcag278

BME Front. 2026 Sep 22;7:0313. doi: 10.34133/bmef.0313. eCollection 2026.

ABSTRACT

Objective: To investigate the clinical efficacy and neural mechanisms of targeted 810-nm transcranial near-infrared stimulation (tNIRS) over the bilateral angular gyrus in Alzheimer's disease (AD). Impact Statement: We provide the first clinical evidence that targeted tNIRS ameliorates AD cognitive deficits by dynamically reconfiguring large-scale brain networks, establishing a precise, mechanistic neuromodulation strategy. Introduction: While tNIRS shows potential for AD, conventional whole-brain irradiation lacks anatomical precision and yields inconsistent outcomes. Resolving these elusive network-level mechanisms requires targeted stimulation paradigms. Methods: In a randomized, sham-controlled trial, 36 biomarker-confirmed AD patients received 810-nm tNIRS targeted at the bilateral angular gyrus or sham stimulation (20 min/side daily, 15 d). Baseline and post-intervention neuropsychological assessments, resting-state functional magnetic resonance imaging (fMRI), and transcranial magnetic stimulation-electroencephalography (TMS-EEG) were utilized to decode cognitive improvements and network dynamics. Results: Targeted tNIRS obviously improved cognition, memory, language, attention, and executive functions while reducing neuropsychiatric symptoms. fMRI revealed enhanced intra-network connectivity within the default mode network (DMN) and promoted functional synergy between the DMN, dorsal attention, frontoparietal, and limbic networks. TMS-EEG demonstrated enhanced information flow and dynamic nodal reconfiguration, specifically boosting prefrontal and temporo-occipital activation. Conclusion: Bilateral angular gyrus targeted tNIRS may effectively mitigate AD cognitive impairment potentially through modulation of functional connectivity and adaptively reconfiguring higher-order cognitive networks, serving as a highly promising clinical neuromodulation therapy.

PMID:42774645 | PMC:PMC13594423 | DOI:10.34133/bmef.0313

Brain Imaging Behav. 2026 Sep 23;20(5):130. doi: 10.1007/s11682-026-01200-3.

ABSTRACT

To investigate the neurovascular coupling (NVC) differences in cerebral small vessel disease (CSVD) with or without cognitive impairment, using arterial transit time corrected cerebral blood flow, dynamic and static regional homogeneity, and explore underlying mechanisms through stepwise multiple linear regression and mediation analysis. Sixty-five CSVD patients and 36 healthy controls underwent resting-state fMRI and arterial spin labeling imaging. Cerebral blood flow, dynamic and static regional homogeneity, and NVC metrics were calculated. Cognitive, depression, and anxiety assessments were conducted. Stepwise multiple linear regression and mediation analysis were performed to understand the relationship between CSVD pathology and cognition status. Altered NVC was mainly distributed in the prefrontal cortex and basal ganglia. Reduced cross-voxel-correlation-coefficients within the whole brain in CSVD were observed. Positive predictors for cognition score included NVC-related metrics in default mode network, precentral gyrus, the whole gray matter and education. Finally, mediation analysis revealed the role of NVC in the relationship between depression and cognitive performance. NVC dysfunction is observed in CSVD. NVC-related abnormality in default mode network, precentral gyrus, the whole gray matter, and education level contribute to global cognition in CSVD. NVC may mediate depression and global cognition.

PMID:42773289 | DOI:10.1007/s11682-026-01200-3

Neuroscience. 2026 Sep 22:S0306-4522(26)00647-0. doi: 10.1016/j.neuroscience.2026.09.036. Online ahead of print.

ABSTRACT

BACKGROUND: Anterior ischemic optic neuropathy (AION) is primarily an optic nerve disorder, but loss of visual afferent input may also be associated with cortical reorganization. Intrinsic neural timescale (INT), derived from resting-state fMRI, indexes the temporal persistence of BOLD activity. We examined whether AION is associated with altered cortical INT and whether the spatial pattern of these alterations aligns with normative molecular and neurochemical architecture.

METHODS: Resting-state fMRI data were analyzed from 30 patients with AION and 25 healthy controls. Voxel-wise INT was estimated from the autocorrelation of BOLD signals, and group differences were tested using Gaussian random field correction. Exploratory network-level comparisons were performed without multiple-comparison correction. Spatial associations with Allen Human Brain Atlas gene expression and PET-derived neurotransmitter maps were assessed using partial least squares regression and spatial permutation tests, respectively.

RESULTS: Compared with healthy controls, patients with AION showed lower INT in the bilateral calcarine cortex, with no regions showing higher INT. Exploratory uncorrected network comparisons yielded lower estimates in the auditory and anterior attention networks. The regional INT contrast aligned with normative transcriptional gradients enriched for synaptic and neuronal processes and with the spatial distributions of several neurotransmitter receptor systems.

CONCLUSIONS: AION was associated with a focal reduction in calcarine INT under the primary operational definition used in this study. Normative transcriptomic and receptor maps provided biological context for the spatial pattern but did not establish patient-specific molecular changes or causal mechanisms.

PMID:42772506 | DOI:10.1016/j.neuroscience.2026.09.036

Biomed Phys Eng Express. 2026 Sep 22. doi: 10.1088/2057-1976/aeaaec. Online ahead of print.

ABSTRACT

Brain disorder (BD) classification based on population graph learning has shown promising performance by jointly modeling resting-state functional MRI (rs-fMRI) data and phenotypic information. Existing graph Transformer-based population graph encoders suffer from high computational cost on densely connected subject graphs and lack an efficient mechanism for global contextual modeling across distant subjects. To address these limitations, we propose HGMA-Net, a hierarchical graph-Mamba hybrid framework for multi-modal brain disorder classification. Specifically, the proposed HGMA module redesigns the shallow encoder stage using a dual-path architecture that combines local graph aggregation with global sequence modeling. A degree-guided node serialization strategy provides a topology-aware ordering of graph nodes, enabling bidirectional Mamba state space models to efficiently capture global contextual information with linear sequence complexity. An adaptive gating mechanism is further introduced to dynamically fuse global sequential features and local graph-structural features. To balance computational efficiency and representational capacity, HGMA is applied only at the first encoder layer, while deeper layers retain TransformerConv for local topology refinement. Experiments on the ABIDE and ADHD-200 datasets demonstrate that HGMA-Net achieves superior classification performance, obtaining AUC scores of 89.14% and 91.70%, respectively. HGMA-Net also retains efficient inference when global contextual modeling is introduced at the shallow encoder stage.

PMID:42772312 | DOI:10.1088/2057-1976/aeaaec

Psychiatry Res Neuroimaging. 2026 Sep 18;364:112336. doi: 10.1016/j.pscychresns.2026.112336. Online ahead of print.

ABSTRACT

Mood disorders including major depressive disorder and bipolar disorder are among the leading causes of disability worldwide, often emerging early in life and following chronic, relapsing courses. The development of these disorders is influenced by a range of familial, environmental, clinical, and genetic risk factors. In recent years, neuroimaging techniques have played a key role in identifying biomarkers of vulnerability among individuals at elevated risk, with the goal of improving early detection and preventive interventions. This systematic review aimed to consolidate neuroimaging findings in populations vulnerable to mood disorders, with a particular focus on resilience as a complementary framework to risk, the latter having historically received far more research attention. Following PRISMA 2020 guidelines, we searched PubMed, Embase, and Scopus, applying an independent duplicate screening process to identify relevant studies based on predefined inclusion and exclusion criteria. A total of 31 studies met the inclusion criteria, spanning structural MRI (sMRI), functional MRI (fMRI), and diffusion MRI (dMRI). Synthesis of these findings highlights neurobiological markers of resilience, including preserved task-related and resting-state fronto-limbic function, enhanced connectivity within neural circuits supporting emotion regulation, and structural integrity in prefrontal regions that may underlie adaptive functioning in individuals at high risk for mood disorders.

PMID:42771937 | DOI:10.1016/j.pscychresns.2026.112336

Elife. 2026 Sep 22;15:RP110234. doi: 10.7554/eLife.110234.

ABSTRACT

The human brain seamlessly integrates internally generated thoughts with incoming sensory information, yet the large-scale networks that support these functions - the internal default network (DN) and external dorsal attention network (dATN) - are traditionally viewed as functionally independent. This raises a crucial question: how does the brain integrate information across these seemingly noninteractive systems? Here, using densely sampled 7T fMRI, individualized resting-state parcellations, and voxel-wise population-receptive-field mapping, we show that these internal/external networks are more interlocked than previously thought. Spontaneous DN and dATN activity during rest is uncorrelated at the network level. However, voxel-scale functional coupling across networks is shaped by the latent visual field preferences of individual voxels in each network, as measured during independent retinotopic mapping. Voxels that share visual field preferences exhibit stronger spontaneous coupling than those with divergent preferences. These retinotopically specific interactions are bivalent: DN voxels with negative (suppressive) visual response amplitudes are anticorrelated with matched (positive) dATN voxels, while those DN voxels with positive response amplitudes are positively correlated. Thus, distinct subpopulations of visually tuned DN voxels participate in spatially specific interactions with the dATN. Further, retinotopic coding is intrinsic to the DN, persisting even during periods when the DN signal is elevated. These findings reveal a latent, voxel-level architecture of retinotopically grounded interactions between the DN and dATN. Taken together, our results suggest that retinotopic coding underpins the dynamic coordination of perception and thought in the human brain.

PMID:42771535 | DOI:10.7554/eLife.110234