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Neuroimage Rep. 2025 Nov 12;5(4):100299. doi: 10.1016/j.ynirp.2025.100299. eCollection 2025 Dec.

ABSTRACT

Cognitive symptoms are common in Parkinson's disease (PD) and have debilitating effects on quality of life and disease trajectory; however, the underlying brain mechanisms remain poorly understood. To address this gap, we investigated the relationship between functional connectivity and cognition at multiple time points using longitudinal functional MRI (fMRI) and cognitive assessments from the Parkinson's Progression Marker Initiative (PPMI). We calculated resting-state functional connectivity across three distinct time points. We analyzed functional connectivity within and between three key cortical brain networks that have been linked with higher-order cognitive function in PD: the frontoparietal network (FPN); the salience network (SAL); and the default mode network (DMN). Global cognitive functioning was assessed with the Montreal Cognitive Assessment (MoCA) at each of the three time points, and this was our primary dependent variable. Linear mixed-effects modeling revealed that decreased FPN-DMN functional connectivity is associated with lower MoCA scores over time. A similar trend was found for SAL-DMN functional connectivity. These relationships were specific to cognition, as there were no significant associations between functional connectivity and motor symptoms, as measured with the Movement Disorders Society-Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III). These findings suggests that cortical connectivity is associated with and may contribute to the progression of cognitive symptoms in PD. Our findings advance knowledge about cognitive changes in PD and emphasize the importance of functional brain network architecture.

PMID:41322670 | PMC:PMC12657728 | DOI:10.1016/j.ynirp.2025.100299

Imaging Neurosci (Camb). 2025 Nov 26;3:IMAG.a.1027. doi: 10.1162/IMAG.a.1027. eCollection 2025.

ABSTRACT

The long-term impact of COVID-19 on the brain is multifaceted, encompassing structural and functional disruptions. A cohesive theory of the underlying mechanisms of the Post-COVID Syndrome (PCS) remains unknown, primarily due to high variability in findings across independent studies. Here, we present a multimodal, cross-sectional MRI analysis of brain morphology (T1-MRI), tissue microstructure (diffusion-MRI), functional connectivity (functional-MRI), and cerebral blood flow (arterial spin labeling MRI) in COVID-recovered patients (CRPs, N=76) and healthy controls (HCs, N = 51). Although the global brain volumes did not differ between the two groups, CRPs showed focal atrophy in the right basal ganglia and limbic structures, along with cortical thinning in paralimbic regions (prefrontal cortex, insula) (p < 0.05). Diffusion MRI analysis revealed reduced fractional anisotropy and elevated radial diffusivity in the uncinate fasciculus and cingulum. No differences were observed in resting-state functional connectivity (RSFC) and cerebral blood flow between HCs and CRPs (p > 0.05). We further investigated the effect of infection severity by stratifying the CRPs into hospitalized (HP; N = 21) and non-hospitalized (NHP; N = 46) groups. The microstructural damage was linked to infection severity, more pronounced in the HPs (p < 0.05). In HPs, RSFC was diminished between components of the default mode network and the insula and caudate as compared with HCs and NHPs (p < 0.05). Results suggest COVID-19 is associated with selective structural and functional alterations in basal ganglia-limbic-cortical circuits, with stronger effects in severe cases. Overall, our findings both validate previously reported neuroimaging biomarkers and reveal new ones associated with the post-COVID syndrome, motivating future hypothesis-driven studies on behavioral correlates and therapeutic interventions.

PMID:41322364 | PMC:PMC12658774 | DOI:10.1162/IMAG.a.1027

Biol Psychiatry Glob Open Sci. 2025 Sep 30;6(1):100623. doi: 10.1016/j.bpsgos.2025.100623. eCollection 2026 Jan.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool to reveal disrupted brain activity in major depressive disorder (MDD), but most studies have focused solely on low-frequency functional fluctuations, ignoring the fact that brain activity is composed of both low-frequency and high-frequency fluctuations. Therefore, we applied a novel approach, namely the power spectral slope (PSS), which captures the characteristics of both low- and high-frequency fluctuations to evaluate brain activity in MDD.

METHODS: rs-fMRI data were collected from 109 patients with MDD (27.29 ± 7.11 years, 75 women) and 78 normal control participants (26.47 ± 6.19 years, 51 women). A subset of 52 patients with MDD also underwent rs-fMRI scanning after a 12-week antidepressant treatment (escitalopram/duloxetine). Both the baseline between-group comparison and follow-up within-group comparison were performed for PSS. A 2-sample t test was used for baseline comparison with a liberal Gaussian random-field correction. The follow-up comparison was tested with paired t test.

RESULTS: Patients with MDD showed significantly more negative PSS compared with normal control participants in the ventral striatum and temporal pole. After treatment, PSS in the ventral striatum increased significantly toward normalization, whereas the temporal pole's slope remained unchanged. No significant correlations were found between PSS and depression severity scores.

CONCLUSIONS: This study demonstrates that MDD is characterized by more negative PSS in key affective regions. The normalization effect of ventral striatum spectral slope following antidepressant treatment suggests a region-specific response. Taken together, the findings suggest that PSS may serve as a novel brain functional marker for MDD.

PMID:41321422 | PMC:PMC12657283 | DOI:10.1016/j.bpsgos.2025.100623

Biol Psychiatry Glob Open Sci. 2025 Sep 30;6(1):100624. doi: 10.1016/j.bpsgos.2025.100624. eCollection 2026 Jan.

ABSTRACT

Understanding the impact of prenatal cannabis exposure (PCE) on brain development is increasingly important given rising cannabis use during pregnancy. Many existing reviews on this topic are more than 5 years old and may not reflect recent social shifts that could impact cannabis use during pregnancy; they also have not utilized the recently available large longitudinal datasets for more robust and population-representative investigations. In this narrative review, we aim to provide an updated and expanded examination of the associations between PCE and magnetic resonance imaging (MRI)-based brain outcomes from in utero development to adolescence. We included studies published after 2019 that used at least one of the following measures: structural MRI, diffusion-weighted imaging, resting-state fMRI, and/or task-based fMRI. Across 9 studies that met criteria, 1 study focused on MRI outcomes in utero, 2 in infancy, and 6 in early adolescence, and only 3 studies included MRI and behavior outcomes. PCE was linked to differences in frontal, parietal, and temporal areas, spanning from in utero to adolescence across multiple MRI modalities. However, in the current state of the literature, detecting a consistent trend on PCE's impact on MRI findings was not possible. Furthermore, we found several divergences in study design: varying approaches to assessment (e.g., self-report vs. urine toxicology); difficulties in accounting for prenatal exposure to multiple substances; limited information on timing, frequency, potency, or mode of consumption; and the influence of parental or postnatal factors. Future research should implement designs that can rigorously capture the abovementioned elements to permit replication and eventual meta-analyses on this critical topic.

PMID:41321421 | PMC:PMC12663006 | DOI:10.1016/j.bpsgos.2025.100624

Biol Psychiatry. 2025 Nov 28:S0006-3223(25)01645-2. doi: 10.1016/j.biopsych.2025.11.016. Online ahead of print.

ABSTRACT

BACKGROUND: Posttraumatic stress disorder (PTSD) is a heterogeneous condition with diverse symptom presentations and emotional experiences. While fear is traditionally viewed as central, growing evidence highlights the role of non-fear-based emotions - such as sadness, guilt, and shame - collectively termed Emotional Pain. This study aimed to identify Emotional Pain and Fear-based PTSD symptom profiles and their neural correlates across two independent samples.

METHODS: In Study 1 (n=838), trauma-exposed individuals with probable PTSD completed the PTSD Checklist for DSM-5 (PCL-5) and subjective ratings of Fear and Emotional Pain. Item-level network analysis was conducted to identify central symptoms and relationships. In Study (n=162), recent trauma survivors with high PTSD symptoms underwent resting-state and task-based functional MRI (fMRI) scans 1-month post-trauma, and completed follow-up clinical assessment at 14-months post-trauma. Connectome-based predictive modeling (CPM) was used to predict chronic symptom severity for Fear and Emotional Pain-based profiles, identified in Study 1.

RESULTS: Emotional Pain was rated as more impairing than Fear by most participants (69%). Symptom networks showed distinct patterns: Fear was associated with flashbacks, nightmares, distressing memories, exaggerated startle, and external avoidance; Emotional Pain was linked to anhedonia, negative beliefs, negative emotions, sleep disturbance and emotional reactivity. CPM predicted chronic Fear-based symptom severity (rho=0.228, p<0.001), but not Emotional Pain (rho=0.167, p=0.055). Predictive features included connections across anterior default-mode, central executive, salience, motor-sensory and subcortical networks.

CONCLUSIONS: Emotional Pain and Fear may represent distinct PTSD dimensions. Disentangling their neural signatures may improve diagnostic precision and guide personalized, mechanism-based interventions for trauma-related psychopathology.

PMID:41319905 | DOI:10.1016/j.biopsych.2025.11.016

Neuroimage. 2025 Nov 27:121579. doi: 10.1016/j.neuroimage.2025.121579. Online ahead of print.

ABSTRACT

Data collection technology in functional magnetic resonance imaging (fMRI) is rapidly developing, leading to continuous growth of spatio-temporal resolution. The need to understand brain dynamics, as it plays a crucial role in understanding brain function, continues to push innovation in this direction as limits on the frequency of data measurement limit the kinds of questions that may be asked. In parallel, researchers continue to amass large volumes of fMRI data using the highest sampling frequencies available with current technology. A common and plausible assumption is that higher measurement frequencies may lead to more informative data about the brain dynamics and help mitigate physiological noise from neurovascularly coupled signal. This assumption leads to the tendency to discard the older datasets collected with lower temporal resolution in favor of more recent collections. Moreover, as we will show, it leads to under-utilizing the current MRI technology by only collecting at the fastest available rate. A recent theoretical study demonstrated that combining high frequency data with data collected at a deliberately slower sampling rate can, in some conditions, lead to gains in information about the dynamics. We hypothesize that similar effects can be observed in fMRI datasets where data is collected at multiple timescales, as opposed to datasets created by subsampling from a single acquisition rate. A resting state fMRI dataset collected from 10 subjects at a slow (2150 ms) and fast (100 ms) repetition time (TR) is analyzed, demonstrating informative gains in predictive power by combining the two. This gain is in contrast to diminishing returns in the single TR dataset performance, where the data has been manually-undersampled to a slower sampling rate and combined with the original. Performance outcomes were also compared in gender prediction across a multi-rate dataset and single rate dataset, with multi-rate results showing gains in composite features. Our experiments demonstrate agreement with the theoretical results in showing that features formed as a combination of slow and fast sampling rates yield greater predictive power than features from either slow or fast rates alone in some settings.

PMID:41318042 | DOI:10.1016/j.neuroimage.2025.121579

Drug Alcohol Depend. 2025 Nov 22;278:112972. doi: 10.1016/j.drugalcdep.2025.112972. Online ahead of print.

ABSTRACT

Alcohol intake disrupts cognitive and sensory processing. However, its effects on the role of individual structures within cortical networks, or on the larger network structure, remain unclear. This acute alcohol administration study addressed this gap using graph theory analysis. Healthy individuals (n = 107, 21-45yrs, 61 women) consumed alcohol (0.08g/dL target BrAC) or a placebo drink in 2 double-blinded sessions and self-reported their perceived intoxication using a visual analog scale. Resting state fMRI was acquired with a Siemens Prisma 3T scanner 30min after consumption. The effect of alcohol on graph theory outcomes in a network of 106 cerebral ROIs was identified using the CONN toolbox. We also determined the association between graph theory metrics and subjective intoxication. Results revealed alcohol 1) significantly decreased global efficiency in several occipital nodes and increased global efficiency for nodes within the frontal and temporal cortex; 2) increased local efficiency at a network level as well as in specific nodes in the temporal and frontal cortices; 3) increased degree in frontal and temporal regions; 4) decreased closeness centrality and increased mean path length in parietal and occipital regions as well at the network level compared with placebo conditions. Additionally, decreases in global efficiency and increases in local efficiency and clustering coefficient in the alcohol vs. placebo condition significantly predicted subjective intoxication. Taken together, results provide new evidence that alcohol intake produces changes in the overall topography of the cerebral network that at least partially underlie individual differences in subjective alcohol response.

PMID:41317510 | DOI:10.1016/j.drugalcdep.2025.112972

J Headache Pain. 2025 Nov 27;26(1):272. doi: 10.1186/s10194-025-02210-0.

ABSTRACT

BACKGROUND: New daily persistent headache (NDPH) is a rare, refractory primary headache with an unclear pathophysiological mechanism. Previous neuroimaging studies on NDPH have been largely limited to 3T MRI, which fails to thoroughly reveal microstructural changes, particularly subregional abnormalities and iron metabolism alterations. Based on this, the present study employs 7T multimodal imaging techniques to investigate structural, functional, and iron metabolism abnormalities in the whole brain and, in particular, the changes in subregions of the limbic system.

METHODS: A total of 23 individuals with NDPH and 23 healthy controls (HCs) underwent 7T MRI, including T1-weighted three-dimensional magnetization-prepared 2 rapid acquisition gradient echo (3D-T1WI-MP2RAGE) and resting-state fMRI (rs-fMRI); among these patients, 19 also underwent quantitative susceptibility mapping (QSM). Structural volumes, functional metrics (fractional amplitude of low-frequency fluctuations [fALFF], regional homogeneity [ReHo]), and iron deposition (assessed via QSM) were analyzed, and their correlations with clinical parameters (e.g., headache history, anxiety/depression scores) were examined.

RESULTS: Compared to HCs, individuals with NDPH exhibited significantly less volume in the right accumbens area and left caudal anterior cingulate cortex after false discovery rate (FDR) correction. Widespread changed fALFF and ReHo values were observed, with correlations to clinical features. QSM values were decreased in right paracentral, right cuneus and left precentral with increasing in left rostral middle frontal.

CONCLUSION: 7T multimodal MRI identifies widespread structural, functional, and iron metabolism abnormalities in NDPH, particularly in limbic subregions, highlighting a “pain-emotion” interaction mechanism. These findings provide preliminary insights into NDPH pathogenesis.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-025-02210-0.

PMID:41310432 | PMC:PMC12659571 | DOI:10.1186/s10194-025-02210-0

Hum Brain Mapp. 2025 Dec 1;46(17):e70424. doi: 10.1002/hbm.70424.

ABSTRACT

Transcranial magnetic stimulation (TMS) guided by magnetic resonance imaging (MRI) has significantly advanced the treatment of mood disorders by enabling precise targeting of brain circuits implicated in their pathophysiology. The integration of neuronavigation systems, which utilize real-time MRI-based coil positioning, has improved spatial targeting accuracy, individualization, and therapeutic outcomes. Despite these advancements, achieving optimal stimulation efficacy requires careful consideration of MRI techniques, including anatomical imaging, functional MRI (fMRI), and connectivity-based methods. Anatomical MRI provides a reliable structural foundation for neuronavigation but lacks specificity regarding functional neural networks implicated in mood disorders. In contrast, fMRI, through task-based and resting-state paradigms, enhances target selection precision by identifying patient-specific neural activity and functional connectivity patterns, although this approach is vulnerable to variability and imaging artifacts. Connectivity-based MRI neuronavigation represents a promising advancement by explicitly targeting disrupted neural networks. This review critically examines recent technological and methodological progress in MRI-guided neuronavigation for TMS, addressing current challenges such as image acquisition quality, co-registration accuracy, artifact mitigation, and practical constraints in clinical settings. Finally, it discusses emerging opportunities and innovations poised to enhance neuronavigation precision, foster wider clinical adoption, and ultimately improve therapeutic outcomes in interventional psychiatry for mood disorders.

PMID:41310980 | DOI:10.1002/hbm.70424

Psychol Med. 2025 Nov 28;55:e363. doi: 10.1017/S0033291725102638.

ABSTRACT

BACKGROUND: Psychomotor disturbance has long been observed in major depressive disorder (MDD) and is thought to be a key indicator of illness course. However, dominant methods of measuring psychomotor disturbance, via self-report and clinician ratings, often lack objectivity and may be less sensitive to subtle psychomotor disturbances. Furthermore, the neural mechanisms of psychomotor disturbance in MDD remain unclear.

METHODS: To address these gaps, we measured psychomotor agitation via a force variability paradigm and collected resting fMRI in 47 individuals with current MDD (cMDD) and 93 individuals with remitted MDD (rMDD). We then characterized whether resting-state cortico-cortical and cortico-subcortical connectivity related to force variability and depressive symptoms.

RESULTS: Behaviorally, individuals with cMDD exhibited greater force variability than rMDD individuals (t(138) = 3.01, p = 0.003, Cohen's d = 0.25). Furthermore, greater force variability was associated with less visuomotor connectivity (r(130) = -0.23, p = 0.009, 95% CI [-0.38, -0.06]). Visuomotor connectivity was significantly reduced in cMDD relative to rMDD (t(130) = -2.77, p = 0.006, Cohen's d = -0.24) and mediated the group difference in force variability (ACME β = -0.06, 95% CI [-0.16, -0.01], p = 0.04).

CONCLUSIONS: Our findings represent a crucial step toward clarifying the pathophysiology of psychomotor agitation in MDD. Specifically, altered visuomotor functional connectivity emerged as a candidate neural mechanism, highlighting a promising direction for future research on dysfunctional visually guided movements in MDD.

PMID:41310957 | DOI:10.1017/S0033291725102638

Chin Med. 2025 Nov 28;20(1):207. doi: 10.1186/s13020-025-01236-5.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) is characterized by dysregulation in brain function and is closely associated with neuroinflammation. Although acupuncture has been shown to improve insomnia symptoms, its underlying mechanisms, particularly at both the macro brain connectivity and corresponding molecular levels, remain unclear METHODS: Forty-eight CID patients were randomly assigned to either an acupuncture group or a waitlist group. Clinical data and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before and after the intervention. Changes in brain connectivity were analyzed using fMRI to assess global brain connectivity (GBC) in each group. Gene expression data from the Allen Human Brain Atlas were utilized to identify important genes contributing to these acupuncture-induced GBC changes. Gene set enrichment analysis was performed to annotate the molecular biological processes involved.

RESULTS: In the acupuncture group, fMRI analysis revealed decreased regional GBC in key regions, such as the pallidum and prefrontal cortex, correlating with symptom relief. In contrast, the waitlist group showed increased regional GBC without symptom relief. Gene set enrichment analysis revealed that specific genes associated with astrocytes and neuroinflammation-related biological processes were linked to the acupuncture-induced changes in GBC. The neuroinflammation-informed GBC-transcriptomic signatures induced by acupuncture were further validated by their significant correlation with reductions in IL-6 levels as insomnia symptoms improved.

CONCLUSION: Acupuncture may remodel brain functional connectivity by regulating neuroinflammation-related pathways, thereby improving insomnia symptoms.

PMID:41310834 | DOI:10.1186/s13020-025-01236-5