Most recent paper

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