Most recent paper

Brain Commun. 2024 Aug 19;6(5):fcae281. doi: 10.1093/braincomms/fcae281. eCollection 2024.

ABSTRACT

Addiction to psychoactive substances is a maladaptive learned behaviour. Contexts surrounding drug use integrate this aberrant mnemonic process and hold strong relapse-triggering ability. Here, we asked where context and salience might be concurrently represented in the brain during retrieval of drug-context paired associations. For this, we developed a morphine-conditioned place preference protocol that allows contextual stimuli presentation inside a magnetic resonance imaging scanner and investigated differences in activity and connectivity at context recall. We found context-specific responses to stimulus onset in multiple brain regions, namely, limbic, sensory and striatal. Differences in functional interconnectivity were found among amygdala, lateral habenula, and lateral septum. We also investigated alterations to resting-state functional connectivity and found increased centrality of the lateral septum in a proposed limbic network, as well as increased functional connectivity of the lateral habenula and hippocampal 'cornu ammonis' 1 region, after a protocol of associative drug-context. Finally, we found that pre- conditioned place preference resting-state connectivity of the lateral habenula and amygdala was predictive of inter-individual conditioned place preference score differences. Overall, our findings show that drug and saline-paired contexts establish distinct memory traces in overlapping functional brain microcircuits and that intrinsic connectivity of the habenula, septum, and amygdala likely underlies the individual maladaptive contextual learning to opioid exposure. We have identified functional maps of acquisition and retrieval of drug-related memory that may support the relapse-triggering ability of opioid-associated sensory and contextual cues. These findings may clarify the inter-individual sensitivity and vulnerability seen in addiction to opioids found in humans.

PMID:39229487 | PMC:PMC11369824 | DOI:10.1093/braincomms/fcae281

Neuroimage Clin. 2024 Aug 29;43:103664. doi: 10.1016/j.nicl.2024.103664. Online ahead of print.

ABSTRACT

BACKGROUND: Increased resting state functional connectivity between regions involved in emotion control with regions with other specializations, e.g. motor control (emotional hyperconnectivity) is one of the most consistent imaging findings in persons suffering from dissociative seizures (DS). The overall goal of this study was to better characterize DS-related emotional hyperconnectivity using dynamic resting state analysis combined with brainstem volumetry to investigate 1. If emotional hyperconnectivity is restricted to a single state. 2. How volume losses within the modulatory and emotional motor subnetworks of the neuromodulatory system influence the expression of the emotional hyperconnectivity.

METHODS: 13 persons with dissociative seizures (PDS) (f/m:10/3, mean age (SD) 44.6 (11.5)) and 15 controls (CON) (f/m:10/5, mean age (SD) 41.7 (13.0)) underwent a mental health test battery and structural and functional imaging at 3 T. Deformation based morphometry was used to assess brain volume loss by extracting the mean Jacobian determinants from 457 brain, forebrain and brainstem structures. The bold signals from 445 brainstem and brain rois were extracted with CONN and a dynamic fMRI analysis combined with graph and hierarchical analysis was used to identify and characterize 9 different brain states. Welch's t tests and Kendall tau tests were used for group comparisons and correlation analyses.

RESULTS: The duration of Brain state 6 was longer in PDS than in CON (93.1(88.3) vs. 23.4(31.2), p = 0.01) and positively correlated with higher degrees of somatization, depression, PTSD severity and dissociation. Its global connectivity was higher in PDS than CON (90.4(3.2) vs 86.5(4.2) p = 0.01) which was caused by an increased connectivity between regions involved in emotion control and regions involved in sense of agency/body control. The brainstem and brainstem-forebrain modulatory and emotional motor subnetworks of the neuromodulatory system were atrophied in PDS. Atrophy severity within the brainstem-forebrain subnetworks was correlated with state 6 dwell time (modulatory: tau = -0.295, p = 0.03; emotional motor: tau = -0.343, p = 0.015) and atrophy severity within the brainstem subnetwork with somatization severity (modulatory: tau = -0.25, p = 0.036; emotional motor: tau = -0.256, p = 0.033).

CONCLUSION: DS-related emotional hyperconnectivity was restricted to state 6 episodes. The remaining states were not different between PDS and CON. The modulatory subnetwork synchronizes brain activity across brain regions. Atrophy and dysfunction within that subnetwork could facilitate the abnormal interaction between regions involved in emotion control with those controlling sense of agency/body ownership during state 6 and contribute to the tendency for somatization in PDS. The emotional motor subnetwork controls the activity of spinal motoneurons. Atrophy and dysfunction within this subnetwork could impair that control resulting in motor symptoms during DS. Taken together, these findings indicate that DS have a neurophysiological underpinning.

PMID:39226702 | DOI:10.1016/j.nicl.2024.103664

Neurology. 2024 Oct 8;103(7):e209816. doi: 10.1212/WNL.0000000000209816. Epub 2024 Sep 3.

ABSTRACT

BACKGROUND AND OBJECTIVES: Despite the success of presurgical network connectivity studies in predicting short-term (1-year) seizure outcomes, later seizure recurrence occurs in some patients with temporal lobe epilepsy (TLE). To uncover contributors to this recurrence, we investigated the relationship between functional connectivity and seizure outcomes at different time points after surgery in these patients.

METHODS: Patients included were clinically diagnosed with unilateral mesial TLE after a standard clinical evaluation and underwent selective amygdalohippocampectomy. Healthy controls had no history of seizures or head injury. Using resting-state fMRI, we assessed the postsurgical functional connectivity node strength, computed as the node's total strength to all other nodes, between seizure-free (Engel Ia-Ib) and nonseizure-free (Engel Ic-IV) acquisitions. The change over time after surgery in different outcome groups in these nodes was also characterized.

RESULTS: Patients with TLE (n = 32, mean age: 43.1 ± 11.9 years; 46.8% female) and 85 healthy controls (mean age: 37.7 ± 13.5 years; 48.2% female) were included. Resting fMRI was acquired before surgery and at least once after surgery in each patient (range 1-4 scans, 5-60 months). Differences between patients with (n = 30) and without (n = 18) seizure freedom were detected in the posterior insula ipsilateral to the resection (I-PIns: 95% CI -154.8 to -50.1, p = 2.8 × 10-4) and the bilateral central operculum (I-CO: 95% CI -163.2 to -65.1, p = 2.6 × 10-5, C-CO: 95% CI -172.7 to -55.8, p = 2.8 × 10-4). In these nodes, only those who were seizure-free had increased node strength after surgery that increased linearly over time (I-CO: 95% CI 1.0-5.2, p = 4.2 × 10-3, C-CO: 95% CI 1.0-5.2, p = 5.5 × 10-3, I-PIns: 95% CI 1.6-5.5, p = 0.9 × 10-3). Different outcome groups were not distinguished by node strength before surgery.

DISCUSSION: The findings suggest that network evolution in the first 5 years after selective amygdalohippocampectomy surgery is related to seizure outcomes in TLE. This highlights the need to identify presurgical and surgical conditions that lead to disparate postsurgical trajectories between seizure-free and nonseizure-free patients to identify potential contributors to long-term seizure outcomes. However, the lack of including other surgical approaches may affect the generalizability of the results.

PMID:39226517 | DOI:10.1212/WNL.0000000000209816

Neurol Sci. 2024 Sep 3. doi: 10.1007/s10072-024-07731-1. Online ahead of print.

ABSTRACT

BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiating MCI from AD.

METHODS: PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis was conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class classification tasks).

FINDINGS: In total, 23 studies, comprising 5,554 participants were enrolled in the study. In the binary classification tasks (twenty studies), the diagnostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 ~ 1.00), with a sensitivity of 0.94 (95%CI: 0.89 ~ 0.97) and a specificity of 0.98 (95%CI: 0.95 ~ 1.00). In the multi-class classification tasks (six studies), the diagnostic accuracy of the ML model was 0.98 (95%CI: 0.98 ~ 0.99) for NC, 0.96 (95%CI: 0.96 ~ 0.96) for early mild cognitive impairment (EMCI), 0.97 (95%CI: 0.96 ~ 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 ~ 0.95) for AD.

CONCLUSIONS: The Rs-fMRI-based ML model can be adapted to multi-class classification tasks. Therefore, multi-center studies with large samples are needed to develop intelligent application tools to promote the development of intelligent ML models for disease diagnosis.

PMID:39225837 | DOI:10.1007/s10072-024-07731-1

Neurol Neurochir Pol. 2024 Sep 3. doi: 10.5603/pjnns.99567. Online ahead of print.

ABSTRACT

INTRODUCTION: Epilepsy is a disease characterized by abnormal paroxysmal bioelectrical activity in the brain cortex and subcortical structures. Seizures per se change brain metabolism in epileptic focus and in distal parts of the brain. However, interictal phenomena can also affect functional connectivity (FC) and brain metabolism in other parts of the brain.

AIM OF STUDY: We hypothesised that epilepsy affects functional connectivity not only among cortical, but also between subcortical, structures of the brain in a resting state condition.

CLINICAL RATIONALE FOR STUDY: Investigating functional connectivity in patients with epilepsy could provide insights into the underlying pathophysiological mechanisms. Better understanding may lead to more effective treatment strategies.

MATERIAL AND METHODS: Functional connectivity was analysed in 35 patients with epilepsy and in 28 healthy volunteers. The group of patients was divided into generalised and focal epilepsy (temporal and extratemporal subgroups). Each patient and healthy volunteer underwent an fMRI resting-state session. During the study, EEG signals were simultaneously recorded with fMRI to facilitate the subsequent detection of potential interictal epileptiform discharges (IEDs). Their potential impact on BOLD signals was mitigated through linear regression. The data was processed and correlation coefficients (FC values) between the BOLD signal from selected structures of the central nervous system were determined and compared between study groups. The results were presented as significant differences in correlation coefficients between brain/subcortical structures in the epilepsy and control groups.

RESULTS: Lower FC values for the epilepsy group compared to the control group were shown for connections related to the MPFC, hippocampus, thalamus, amygdala, and the parahippocampal gyrus.

CONCLUSIONS: Epilepsy alters the functional connectivity of resting state subcortical networks. Patterns of pathological changes of FC differ between epilepsy subtypes, with predominant lower FC between the hippocampus, parahippocampal gyrus, amygdala and thalamus in patients with epilepsy.

CLINICAL IMPLICATIONS: This study suggests that epilepsy affects subcortical structures. Identifying distinct patterns of altered FC in epilepsy subtypes may help to tailor treatment strategies. Changes in FC detected by fMRI may precede clinical symptoms, aiding in the early diagnosis of cognitive and emotional disorders in focal epilepsy.

PMID:39225430 | DOI:10.5603/pjnns.99567

Hum Brain Mapp. 2024 Sep;45(13):e70005. doi: 10.1002/hbm.70005.

ABSTRACT

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

PMID:39225381 | DOI:10.1002/hbm.70005

Hum Brain Mapp. 2024 Sep;45(13):e70015. doi: 10.1002/hbm.70015.

ABSTRACT

Decreasing body mass index (BMI) reduces head motion in resting-state fMRI (rs-fMRI) data. Yet, the mechanism by which BMI affects head motion remains poorly understood. Understanding how BMI interacts with respiration to affect head motion can improve head motion reduction strategies. A total of 254 patients with back pain were included in this study, each of whom had two visits (interval time = 13.85 ± 7.81 weeks) during which two consecutive re-fMRI scans were obtained. We investigated the relationships between head motion and demographic and pain-related characteristics-head motion was reliable across scans and correlated with age, pain intensity, and BMI. Multiple linear regression models determined that BMI was the main determinant in predicting head motion. BMI was also associated with two features derived from respiration signal. Anterior-posterior and superior-inferior motion dominated both overall motion magnitude and the coupling between motion and respiration. BMI interacted with respiration to influence motion only in the pitch dimension. These findings indicate that BMI should be a critical parameter in both study designs and analyses of fMRI data.

PMID:39225333 | DOI:10.1002/hbm.70015

Front Neurosci. 2024 Aug 19;18:1455131. doi: 10.3389/fnins.2024.1455131. eCollection 2024.

ABSTRACT

BACKGROUND: Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks.

METHODS: Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA).

RESULTS: Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN).

CONCLUSION: Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis.

PMID:39224578 | PMC:PMC11366611 | DOI:10.3389/fnins.2024.1455131

Front Neurosci. 2024 Aug 19;18:1425032. doi: 10.3389/fnins.2024.1425032. eCollection 2024.

ABSTRACT

BACKGROUND: Individualized cortical functional networks parcellation has been reported as highly reproducible at 3.0 T. However, in view of the complexity of cortical networks and the greatly increased sensitivity provided by ultra-high field 5.0 T MRI, the parcellation consistency between different magnetic fields is unclear.

PURPOSE: To explore the consistency and stability of individualized cortical functional networks parcellation at 3.0 T and 5.0 T MRI based on spatial and functional connectivity analysis.

MATERIALS AND METHODS: Thirty healthy young participants were enrolled. Each subject underwent resting-state fMRI at both 3.0 T and 5.0 T in a random order in less than 48 h. The individualized cortical functional networks was parcellated for each subject using a previously proposed iteration algorithm. Dice coefficient was used to evaluate the spatial consistency of parcellated networks between 3.0 T and 5.0 T. Functional connectivity (FC) consistency was evaluated using the Euclidian distance and Graph-theory metrics.

RESULTS: A functional cortical atlas consisting of 18 networks was individually parcellated at 3.0 T and 5.0 T. The spatial consistency of these networks at 3.0 T and 5.0 T for the same subject was significantly higher than that of inter-individuals. The FC between the 18 networks acquired at 3.0 T and 5.0 T were highly consistent for the same subject. Positive cross-subject correlations in Graph-theory metrics were found between 3.0 T and 5.0 T.

CONCLUSION: Individualized cortical functional networks at 3.0 T and 5.0 T showed consistent and stable parcellation results both spatially and functionally. The 5.0 T MR provides finer functional sub-network characteristics than that of 3.0 T.

PMID:39224574 | PMC:PMC11366602 | DOI:10.3389/fnins.2024.1425032

Front Psychiatry. 2024 Aug 19;15:1428535. doi: 10.3389/fpsyt.2024.1428535. eCollection 2024.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored.

OBJECTIVE: To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex.

METHODS: In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD.

RESULTS: tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition.

CONCLUSIONS: The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

PMID:39224475 | PMC:PMC11366601 | DOI:10.3389/fpsyt.2024.1428535

IEEE Trans Med Imaging. 2024 Sep 2;PP. doi: 10.1109/TMI.2024.3453419. Online ahead of print.

ABSTRACT

Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.

PMID:39222450 | DOI:10.1109/TMI.2024.3453419

Brain Imaging Behav. 2024 Sep 2. doi: 10.1007/s11682-024-00909-3. Online ahead of print.

ABSTRACT

Brain entropy (BEN), which measures the amount of information in brain activity, provides a novel perspective for evaluating brain function. Recent studies using resting-state functional magnetic resonance imaging (fMRI) have shown that BEN during rest can help characterize brain function alterations in schizophrenia (SCZ). However, there is a lack of research on BEN using task-evoked fMRI to explore task-dependent cognitive deficits in SCZ. In this study, we evaluate whether the reduced working memory (WM) capacity in SCZ is possibly associated with dynamic changes in task BEN during tasks with high cognitive demands. We analyzed data from 15 patients with SCZ and 15 healthy controls (HC), calculating task BEN from their N-back task fMRI scans. We then examined correlations between task BEN values, clinical symptoms, 2-back task performance, and neuropsychological test scores. Patients with SCZ exhibited significantly reduced task BEN in the cerebellum, hippocampus, parahippocampal gyrus, thalamus, and the middle and superior frontal gyrus (MFG and SFG) compared to HC. In HC, significant positive correlations were observed between task BEN and 2-back accuracy in several brain regions, including the MFG and SFG; such correlations were absent in patients with SCZ. Additionally, task BEN was negatively associated with scores for both positive and negative symptoms in areas including the parahippocampal gyrus among patients with SCZ. In conclusion, our findings indicate that a reduction in BEN within prefrontal and hippocampal regions during cognitively demanding tasks may serve as a neuroimaging marker for SCZ.

PMID:39222212 | DOI:10.1007/s11682-024-00909-3

Proc SPIE Int Soc Opt Eng. 2024 Feb;12926:129260T. doi: 10.1117/12.3006231. Epub 2024 Apr 2.

ABSTRACT

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well-defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

PMID:39220214 | PMC:PMC11364407 | DOI:10.1117/12.3006231

Biol Psychiatry. 2024 Aug 30:S0006-3223(24)01551-8. doi: 10.1016/j.biopsych.2024.08.018. Online ahead of print.

ABSTRACT

BACKGROUND: Glutathione (GSH) is a crucial antioxidant in the human brain. Although proton magnetic resonance spectroscopy (MRS) using the MEscher-GArwood Point RESolved Spectroscopy (MEGA-PRESS) sequence is highly recommended, limited literature has measured cortical GSH using this method in major psychiatric disorders.

METHODS: By combining MRS using the MEGA-PRESS and resting-state functional magnetic resonance imaging, we quantified brain GSH and glutamate in the medial prefrontal cortex (mPFC) and precuneus and explore relationships between the GSH levels and intrinsic neuronal activity as well as clinical symptoms among the three groups of healthy controls (HCs, N=30), major depressive disorder (MDD, N=28), and obsessive-compulsive disorder (OCD, N=28).

RESULTS: GSH concentrations were lower in both the mPFC and precuneus in both the MDD and OCD groups compared to HCs. In HCs, positive correlations were noted between the GSH and glutamate levels, and between GSH and fractional amplitude of low-frequency fluctuations (fALFF) in both regions. However, while these correlations were absent in both patient groups, they showed a weak positive correlation between glutamate and fALFF values. Moreover, GSH levels negatively correlated with depressive and compulsive symptoms in MDD and OCD, respectively.

CONCLUSIONS: These findings suggest that reduced GSH levels and an imbalance between GSH and glutamate could increase oxidative stress and alter neurotransmitter signaling, leading to disruptions in GSH-related neurochemical-neuronal coupling and psychopathologies across MDD and OCD. Understanding these mechanisms could provide valuable insights into the underlying processes of these disorders, potentially becoming a springboard for future directions and advancing our knowledge of their neurobiological foundations.

PMID:39218137 | DOI:10.1016/j.biopsych.2024.08.018

Psychiatry Res Neuroimaging. 2024 Aug 24;344:111880. doi: 10.1016/j.pscychresns.2024.111880. Online ahead of print.

ABSTRACT

BACKGROUND: Major Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity.

METHODS: Preprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox.

RESULTS: The obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05.

LIMITATIONS: All participants were Chinese.

CONCLUSIONS: Collectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

PMID:39217670 | DOI:10.1016/j.pscychresns.2024.111880

Mol Brain. 2024 Aug 30;17(1):60. doi: 10.1186/s13041-024-01136-z.

ABSTRACT

Mild Cognitive Impairment (MCI) is a neurological condition characterized by a noticeable decline in cognitive abilities that falls between normal aging and dementia. Along with some biomarkers like GAP-43, Aβ, tau, and P-tau, brain activity and connectivity are ascribed to MCI; however, the link between brain connectivity changes and such biomarkers in MCI is still being investigated. This study explores the relationship between biomarkers like GAP-43, Aβ, tau, and P-tau, and brain connectivity. We enrolled 25 Participants with normal cognitive function and 23 patients with MCI. Levels of GAP-43, Aβ1-42, t-tau, and p-tau181p in the CSF were measured, and functional connectivity measures including ROI-to-voxel (RV) correlations and the DMN RV-ratio were extracted from the resting-state fMRI data. P-values below 0.05 were considered significant. The results showed that in CN individuals, higher connectivity within the both anterior default mode network (aDMN) and posterior DMN (pDMN) was associated with higher levels of the biomarker GAP-43. In contrast, MCI individuals showed significant negative correlations between DMN connectivity and levels of tau and P-tau. Notably, no significant correlations were found between Aβ levels and connectivity measures in either group. These findings suggest that elevated levels of GAP-43 indicate increased functional connectivity in aDMN and pDMN. Conversely, elevated levels of tau and p-tau can disrupt connectivity through various mechanisms. Thus, the accumulation of tau and p-tau can lead to impaired neuronal connectivity, contributing to cognitive decline.

PMID:39215335 | DOI:10.1186/s13041-024-01136-z

Neuroimage. 2024 Aug 28:120811. doi: 10.1016/j.neuroimage.2024.120811. Online ahead of print.

ABSTRACT

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

PMID:39214436 | DOI:10.1016/j.neuroimage.2024.120811

J Alzheimers Dis. 2024 Aug 26. doi: 10.3233/JAD-231360. Online ahead of print.

ABSTRACT

BACKGROUND: A range of imaging modalities have reported Alzheimer's disease-related abnormalities in individuals experiencing subjective cognitive decline (SCD). However, there has been no consistent local abnormality identified across multiple neuroimaging modalities for SCD.

OBJECTIVE: We aimed to investigate the convergent local alterations in amyloid-β (Aβ) deposition, glucose metabolism, and resting-state functional MRI (RS-fMRI) metrics in SCD.

METHODS: Fifty SCD patients (66.4±5.7 years old, 19 men [38%]) and 15 normal controls (NC) (66.3±4.4 years old, 5 men [33.3%]) were scanned with both [18F]-florbetapir PET and [18F]-fluorodeoxyglucose PET, as well as simultaneous RS-fMRI from February 2018 to November 2018. Voxel-wise metrics were retrospectively analyzed, including Aβ deposition, glucose metabolism, amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality(DC).

RESULTS: The SCD group showed increased Aβ deposition and glucose metabolism (p < 0.05, corrected), as well as decreased ALFF, ReHo, and DC (p < 0.05, uncorrected) in the left dorsal precuneus (dPCu). Furthermore, the dPCu illustrated negative resting-state functional connectivity with the default mode network. Regarding global Aβ deposition positivity, the Aβ deposition in the left dPCu showed a gradient change, i.e., Aβ positive SCD > Aβ negative SCD > Aβ negative NC. Additionally, both Aβ positive SCD and Aβ negative SCD showed increased glucose metabolism and decreased RS-fMRI metrics in the dPCu.

CONCLUSIONS: The dorsal precuneus, an area implicated in early AD, shows convergent neuroimaging alterations in SCD, and might be more related to other cognitive functions (e.g., unfocused attention) than episodic memory.

PMID:39213059 | DOI:10.3233/JAD-231360

Schizophr Bull. 2024 Aug 30:sbae142. doi: 10.1093/schbul/sbae142. Online ahead of print.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors.

STUDY DESIGN: We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations.

STUDY RESULTS: Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049).

CONCLUSIONS: Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

PMID:39212653 | DOI:10.1093/schbul/sbae142

Sci Rep. 2024 Aug 29;14(1):20120. doi: 10.1038/s41598-024-71174-z.

ABSTRACT

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

PMID:39209988 | DOI:10.1038/s41598-024-71174-z