Most recent paper

Transl Psychiatry. 2024 Aug 21;14(1):337. doi: 10.1038/s41398-024-03050-3.

ABSTRACT

Altered functioning of the bed nucleus of the stria terminalis (BNST) may play a critical role in the etiology of posttraumatic stress disorder (PTSD). Chronic stressors such as racial discrimination and lifetime trauma are associated with an increased risk for PTSD, but it is unknown whether they influence the relationship between BNST functioning and PTSD. We investigated acute post-trauma BNST resting-state functional connectivity (rsFC) as a predictor of future PTSD symptoms in Black trauma survivors. We also examined whether racial discrimination and lifetime trauma moderated the relationship between BNST rsFC and PTSD symptoms. Black adults (N = 95; 54.7% female; mean age = 34.04) were recruited from an emergency department after experiencing a traumatic injury (72.6% were motor vehicle accidents). Two-weeks post-injury, participants underwent a resting-state fMRI scan and completed questionnaires evaluating their PTSD symptoms as well as lifetime exposure to racial discrimination and trauma. Six-months post-injury, PTSD symptoms were reassessed. Whole brain seed-to-voxel analyses were conducted to examine BNST rsFC patterns. Greater rsFC between the BNST and the posterior cingulate cortex, precuneus, left angular gyrus, and hippocampus prospectively predicted six-month PTSD symptoms after adjusting for sex, age, education, and baseline PTSD symptoms. Acute BNST rsFC was a stronger predictor of PTSD symptoms in individuals who experienced more racial discrimination and lifetime trauma. Thus, in the acute aftermath of a traumatic event, the BNST could be a key biomarker of risk for PTSD in Black Americans, particularly for individuals with a greater history of racial discrimination or previous trauma exposure.

PMID:39169008 | DOI:10.1038/s41398-024-03050-3

Exp Gerontol. 2024 Aug 19:112547. doi: 10.1016/j.exger.2024.112547. Online ahead of print.

ABSTRACT

BACKGROUND: While patients affected by mild cognitive impairment (MCI) exhibit characteristic voxel-mirrored homotopic connectivity (VMHC) alterations, the ability of such VMHC abnormalities to predict the diagnosis of MCI in these patients remains uncertain. As such, this study was performed to evaluate the potential role of VMHC abnormalities in the diagnosis of MCI.

METHODS: MCI patients and healthy controls (HCs) were enrolled and subjected to resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing. VMHC and support vector machine (SVM) techniques were then used to examine the collected imaging data.

RESULTS: Totally, 53 MCI patients and 68 healthy controls were recruited. Compared to HCs, MCI patients presented with an increase in postcentral gyrus VMHC. SVM classification demonstrated the ability of postcentral gyrus VMHC values to classify HCs and MCI patients with accuracy, sensitivity, and specificity values of 63.64 %, 71.69 %, and 89.71 %, respectively.

CONCLUSION: VMHC abnormalities in the postcentral gyrus may be mechanistically involved in the pathophysiological progression of MCI patients, and these abnormal VMHC patterns may also offer utility as a neuroimaging biomarker for MCI patient diagnosis.

PMID:39168359 | DOI:10.1016/j.exger.2024.112547

Neuroscience. 2024 Aug 19:S0306-4522(24)00413-5. doi: 10.1016/j.neuroscience.2024.08.027. Online ahead of print.

ABSTRACT

OBJECTIVE: Constipation frequently occurs in patients with Parkinson's disease (PD) and may be related to cognitive and emotional disorders. The aim of this study is to investigate the clinical and brain functional characteristics of patients with PD presenting with constipation.

METHODS: The motor and non-motor symptoms of patients with PD were evaluated, and a resting-state functional magnetic resonance imaging (RS-fMRI) study was conducted based on propensity score matching. Alterations in brain function were analyzed using regional homogeneity (ReHo) and functional connectivity (FC).

RESULTS: Compared with patients without constipation (PD-NC group), patients with constipation (PD-C group) had more serious motor and non-motor symptoms (including cognitive and emotional disorders along with visual hallucinations). Further, emotional and cognitive disorders were correlated with the occurrence of constipation in patients with PD. Compared with the PD-NC group, the PD-C group showed a reduced ReHo of the right insula and bilateral orbitofrontal cortex (OFC), increased ReHo of the left postcentral gyrus, and enhanced FC between the right OFC and the left middle temporal gyrus (MTG) and middle occipital gyrus (MOG). Additionally, the activity of the OFC and insula was significantly correlated with the constipation, mood, and cognitive levels of patients with PD.

CONCLUSIONS: Constipation in patients with PD is closely related to emotional and cognitive impairments, abnormal activity and FC of brain regions such as the right insula and bilateral OFC may play an important role in this.

PMID:39168174 | DOI:10.1016/j.neuroscience.2024.08.027

Exp Neurol. 2024 Aug 19:114928. doi: 10.1016/j.expneurol.2024.114928. Online ahead of print.

ABSTRACT

INTRODUCTION: After mild traumatic brain injury (mTBI), the brain is labile for weeks and months and vulnerable to repeated concussions. During this time, patients are exposed to everyday circumstances that, in themselves, affect brain metabolism and blood flow and neural processing. How commonplace activities interact with the injured brain is unknown. The present study in an animal model investigated the extent to which three commonly experienced exposures-daily caffeine usage, chronic sleep loss, and chronic sleep aid medication-affect the injured brain in the chronic phase.

METHODS: Subclinical trauma by repeated mTBIs was produced by our head rotational acceleration injury model, which causes brain injury consistent with the mechanism of concussion in humans. Forty-eight hours after a third mTBI, chronic administrations of caffeine, sleep restriction, or zolpidem (sedative hypnotic) began and were continued for 70 days. On Days 30 and 60 post injury, resting state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were performed.

RESULTS: Chronic caffeine, sleep restriction, and zolpidem each changed the subclinical brain characteristics of mTBI at both 30 and 60 days post injury, detected by different MRI modalities. Each treatment caused microstructural alterations in DTI metrics in the insular cortex and retrosplenial cortex compared with mTBI, but also uniquely affected other gray and white matter regions. Zolpidem administration affected the largest number of individual structures in mTBI at both 30 and 60 days, and not necessarily toward normalization (sham treatment). Chronic sleep restriction changed local functional connectivity at 30 days in diametrical opposition to chronic caffeine ingestion, and both treatment outcomes were different from sham, mTBI-only and zolpidem comparisons. The results indicate that commonly encountered exposures modify subclinical brain activity and structure long after healing is expected to be complete.

CONCLUSIONS: Changes in activity and structure detected by fMRI are widely understood to reflect changes in the functions of the affected region which conceivably underlie mTBI neuropathology and symptomatology in the chronic phase after injury.

PMID:39168169 | DOI:10.1016/j.expneurol.2024.114928

Neural Netw. 2024 Aug 3;179:106592. doi: 10.1016/j.neunet.2024.106592. Online ahead of print.

ABSTRACT

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.

PMID:39168070 | DOI:10.1016/j.neunet.2024.106592

Front Psychiatry. 2024 Aug 6;15:1458624. doi: 10.3389/fpsyt.2024.1458624. eCollection 2024.

ABSTRACT

BACKGROUND: The development of neuroimaging biomarkers in patients with schizophrenia (SCZ) requires a refined clinical characterization. A limitation of the neuroimaging literature is the partial uptake of progress in characterizing disease-related features, particularly negative symptoms (NS) and cognitive impairment (CI). In the present study, we assessed NS and CI using up-to-date instruments and investigated the associations of abnormalities in brain resting-state (rs)-activity with disease-related features.

METHODS: Sixty-two community-dwelling SCZ subjects participated in the study. Multiple regression analyses were performed with the rs-activity of nine regions of interest as dependent variables and disease-related features as explanatory variables.

RESULTS: Attention/vigilance deficits were negatively associated with dorsal anterior cingulate rs-activity and, together with depression, were positively associated with right dorsolateral prefrontal cortex rs-activity. These deficits and impairment of Reasoning/problem-solving, together with conceptual disorganization, were associated with right inferior parietal lobule and temporal parietal junction rs-activity. Independent of other features, the NS Expressive Deficit domain was associated with the left ventral caudate, while the Motivational Deficit was associated with the dorsal caudate rs-activity.

CONCLUSION: Neurocognitive deficits and the two negative symptom domains are associated with different neural markers. Replications of these findings could foster the identification of clinically actionable biomarkers of poor functional outcomes.

PMID:39165501 | PMC:PMC11333936 | DOI:10.3389/fpsyt.2024.1458624

Sci Rep. 2024 Aug 20;14(1):19232. doi: 10.1038/s41598-024-68490-9.

ABSTRACT

Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks' contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.

PMID:39164353 | DOI:10.1038/s41598-024-68490-9

Neuroscience. 2024 Aug 17:S0306-4522(24)00404-4. doi: 10.1016/j.neuroscience.2024.08.018. Online ahead of print.

ABSTRACT

Psychiatric disturbances are commonly associated with adult-onset isolated dystonia (AOID); however, the mechanisms underlying psychiatric abnormalities in AOID remain unknown. We aimed to investigate the structural and functional brain changes in AOID patients with anxiety, and identify imaging biomarkers for diagnosing anxiety. Structural and functional magnetic resonance was performed on 69 AOID patients and 35 healthy controls (HCs). The Hamilton Anxiety Scale (HAMA) was used to assess anxiety symptoms in AOID patients and assign patients to AOID with and without anxiety groups. Group differences in grey matter volume, amplitude of low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity (ReHo) were evaluated. Area under the receiver operating characteristic curve (ROC AUC) was used as a metric to identify imaging biomarkers for diagnosing anxiety. AOID patients with anxiety exhibited an increased ALFF and ReHo in the left angular gyrus (ANG.L) compared with those without and HCs (voxel P<0.001 and cluster P<0.05, corrected using GRF). A significant positive correlation was observed between ALFF (r = 0.627, P<0.001) and ReHo (r = 0.515, P<0.001) in the ANG.L and HAMA scores in AOID patients. ALFF and ReHo in the ANG.L exhibited an ROC AUC of 0.904 and 0.851, respectively, in distinguishing AOID patients with anxiety from those without and an ROC AUC of 0.887 and 0.853, respectively, in distinguishing AOID patients with anxiety from HCs. These findings provide new insights into the pathophysiology of psychiatric disturbances and highlight potential candidate biomarkers for identifying anxiety in AOID patients.

PMID:39159839 | DOI:10.1016/j.neuroscience.2024.08.018

J Neurosci Res. 2024 Aug;102(8):e25376. doi: 10.1002/jnr.25376.

ABSTRACT

Disrupted connectivity in the default mode network (DMN) during resting-state functional MRI (rs-fMRI) is well-documented in schizophrenia (SCZ). The amygdala, a key component in the neurobiology of SCZ, comprises distinct subregions that may exert varying effects on the disorder. This study aimed to investigate variations in functional connectivity (FC) between distinct amygdala subregions and the DMN in SCZ individuals and explore the effects of treatment on these connections. Fifty-six SCZ patients and 51 healthy controls underwent FC analysis and questionnaire surveys during resting state. The amygdala was selected as the region of interest (ROI) and subdivided into four parts. Changes in FC were examined, and correlations between questionnaire scores and brain activity were explored. Pre-treatment, SCZ patients exhibited reduced FC between the amygdala and DMN compared to HCs. After treatment, significant differences persisted in the right medial amygdala, while other regions did not differ significantly from controls. In addition, PANSS scores positively correlated with FC between the Right Medial Amygdala and the left SMFC (r = .347, p = .009), while RBANS5A scores showed a positive correlation with FC between the Left Lateral Amygdala and the right MTG (rho = -.347, p = .009). The rsFC between the amygdala and the DMN plays a crucial role in the treatment mechanisms of SCZ. This could provide a promising predictive indicator for understanding the neural mechanisms behind treatment and symptomatic improvement.

PMID:39158151 | DOI:10.1002/jnr.25376

Front Syst Neurosci. 2024 Aug 2;18:1425491. doi: 10.3389/fnsys.2024.1425491. eCollection 2024.

ABSTRACT

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

PMID:39157289 | PMC:PMC11327057 | DOI:10.3389/fnsys.2024.1425491

Front Neurosci. 2024 Aug 2;18:1381722. doi: 10.3389/fnins.2024.1381722. eCollection 2024.

ABSTRACT

INTRODUCTION: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.

METHODS: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets.

RESULTS: Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively.

DISCUSSION: This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

PMID:39156630 | PMC:PMC11327036 | DOI:10.3389/fnins.2024.1381722

J Pain Res. 2024 Aug 12;17:2629-2638. doi: 10.2147/JPR.S465018. eCollection 2024.

ABSTRACT

PURPOSE: Zoster-associated pain (ZAP) is frequently concomitant with psychiatric comorbidities. However, the underlying neuropathological mechanisms of ZAP with psychiatric comorbidities remain poorly understood.

PATIENTS AND METHODS: Rest-stating functional MRI (rs-fMRI) data from 41 ZAP patients without anxiety or depression (noA/D-ZAP), 11 ZAP patients with anxiety or depression (A/D-ZAP) and 29 healthy controls (HCs) were acquired. Degree centrality (DC) based on rs-fMRI was used to explore the node changes in the brain functional network in these subjects. Moreover, correlations and receiver operating characteristic curve analysis were performed.

RESULTS: One-way analysis of variance revealed abnormal DC values in the right middle frontal gyrus (MFG) and bilateral precuneus among the three groups. Compared with HCs, A/D-ZAP showed increased DC values in the bilateral pons, while noA/D-ZAP showed increased DC values in the right pons, left brainstem and rectal gyrus and decreased DC values in the right cingulate gyrus and bilateral precuneus. A/D-ZAP showed increased DC values in the left MFG and precentral gyrus (PG) compared with noA/D-ZAP. The DC value of the left pons in A/D-ZAP was positively correlated with the self-rating anxiety scale score. Areas under the curve of DC values in the left PG and MFG for distinguishing A/D-ZAP from the noA/D-ZAP group were 0.907 and 1.000, respectively.

CONCLUSION: This study revealed the node differences in the brain functional network of ZAP patients with or without psychiatric comorbidities. In particular, abnormal DC values of the left MFG and PG may play an important role in the neuropathologic mechanism of the disease.

PMID:39155954 | PMC:PMC11328853 | DOI:10.2147/JPR.S465018

Brain Struct Funct. 2024 Aug 19. doi: 10.1007/s00429-024-02847-8. Online ahead of print.

ABSTRACT

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103 Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

PMID:39155311 | DOI:10.1007/s00429-024-02847-8

J Affect Disord. 2024 Aug 16:S0165-0327(24)01243-6. doi: 10.1016/j.jad.2024.08.030. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients.

METHODS: We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age.

RESULTS: SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001).

LIMITATIONS: Evaluation of brain dynamics was constrained by MRI temporal resolution.

CONCLUSIONS: Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

PMID:39154985 | DOI:10.1016/j.jad.2024.08.030

J Affect Disord. 2024 Aug 16:S0165-0327(24)01306-5. doi: 10.1016/j.jad.2024.08.087. Online ahead of print.

ABSTRACT

BACKGROUND: Although there are a few first-line treatment options for bipolar depression, none are rapid-acting. A new rTMS protocol, Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT®), has been shown to have a rapid antidepressant effect in major depressive disorder (MDD). We examined the preliminary safety, tolerability, and efficacy of SAINT for the treatment of depression in a small sample of persons with treatment-resistant bipolar I disorder.

METHODS: Participants with treatment-resistant bipolar I disorder currently experiencing moderate to severe depression were treated with open-label SAINT. Resting-state functional MRI (fMRI) was used to generate individualized treatment targets for each participant based on the region of the left dorsolateral prefrontal cortex most anticorrelated with the subgenual anterior cingulate cortex. Participants were treated with 10 iTBS sessions daily, with 50-min intersession intervals, for up to 5 consecutive days. The primary outcome was change in Montgomery-Åsberg Depression Rating Scale (MADRS) from baseline to immediate follow-up after treatment.

RESULTS: We treated 10 participants and found a mean reduction of 16.9 in MADRS scores, with a 50 % response rate and 40 % remission rate immediately following treatment. 60 % of participants met remission criteria within the 1-month period following treatment. No serious adverse events, manic episodes, or cognitive side effects were observed.

LIMITATIONS: Our study has a limited sample size and larger samples are needed to confirm safety and efficacy.

CONCLUSIONS: SAINT has shown preliminary feasibility, safety, tolerability, and efficacy in treating treatment-resistant bipolar I depression. Double-blinded sham-controlled trials with larger samples are needed to confirm safety and efficacy.

PMID:39154984 | DOI:10.1016/j.jad.2024.08.087

Neuroscience. 2024 Aug 16:S0306-4522(24)00392-0. doi: 10.1016/j.neuroscience.2024.08.013. Online ahead of print.

ABSTRACT

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

PMID:39154845 | DOI:10.1016/j.neuroscience.2024.08.013

Med Image Anal. 2024 Aug 8;97:103297. doi: 10.1016/j.media.2024.103297. Online ahead of print.

ABSTRACT

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

PMID:39154619 | DOI:10.1016/j.media.2024.103297

Neurosci Lett. 2024 Aug 15:137943. doi: 10.1016/j.neulet.2024.137943. Online ahead of print.

ABSTRACT

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

PMID:39153526 | DOI:10.1016/j.neulet.2024.137943

Dev Cogn Neurosci. 2024 Aug 15;69:101438. doi: 10.1016/j.dcn.2024.101438. Online ahead of print.

ABSTRACT

This systematic review considered evidence of children's and adolescents' typical brain connectivity development studied through resting-state functional magnetic resonance imaging (rs-fMRI). With aim of understanding the state of the art, what has been researched thus far and what remains unknown, this paper reviews 58 studies from 2013 to 2023. Considering the results, rs-fMRI stands out as an appropriate technique for studying language and attention within cognitive domains, and personality traits such as impulsivity and empathy. The most used analyses encompass seed-based, independent component analysis (ICA), the amplitude of the low frequency fluctuations (ALFF), and fractional ALFF (fALFF). The findings highlight key themes, including age-related changes in intrinsic connectivity, sex-specific patterns, and the relevance of the Default Mode Network (DMN). Overall, there is a need for longitudinal approaches to trace the typical developmental trajectory of neural networks from childhood through adolescence with fMRI at rest.

PMID:39153422 | DOI:10.1016/j.dcn.2024.101438

Cereb Cortex. 2024 Aug 1;34(8):bhae327. doi: 10.1093/cercor/bhae327.

ABSTRACT

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric condition that is difficult to treat due to our limited understanding of its pathophysiology. Functional connectivity in brain networks, as evaluated through neuroimaging studies, plays a pivotal role in understanding OCD. While both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been extensively employed in OCD research, few have fully synthesized their findings. To bridge this gap, we reviewed 166 studies (10 EEG, 156 fMRI) published up to December 2023. In EEG studies, OCD exhibited lower connectivity in delta and alpha bands, with inconsistent findings in other frequency bands. Resting-state fMRI studies reported conflicting connectivity patterns within the default mode network (DMN) and sensorimotor cortico-striato-thalamo-cortical (CSTC) circuitry. Many studies observed decreased resting-state connectivity between the DMN and salience network (SN), implicating the 'triple network model' in OCD. Task-related hyperconnectivity within the DMN-SN and hypoconnectivity between the SN and frontoparietal network suggest OCD-related cognitive inflexibility, potentially due to triple network dysfunction. In conclusion, our review highlights diverse connectivity differences in OCD, revealing complex brain network interplay that contributes to symptom manifestation. However, the presence of conflicting findings underscores the necessity for targeted research to achieve a comprehensive understanding of the pathophysiology of OCD.

PMID:39152672 | PMC:PMC11329673 | DOI:10.1093/cercor/bhae327