Most recent paper

Neuroscience. 2024 Dec 25:S0306-4522(24)00752-8. doi: 10.1016/j.neuroscience.2024.12.045. Online ahead of print.

ABSTRACT

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of brain activity in a resting state, which can find the interrelationships, functional connections, and network characteristics among brain regions of the patients. In this paper, a brain functional connectivity matrix is constructed using Pearson correlation based on the characteristics of multi-site Rs-fMRI data and brain atlas, and an adaptive propagation operator graph convolutional network (APO-GCN) model is designed. The APO-GCN model can automatically adjust the propagation operator in each hidden layer according to the data features to control the expressive power of the model. By adaptively learning effective information in the graph, this model significantly improves its ability to capture complex graph structural patterns. The experimental results on Rs-fMRI data from 1601 participants (830 MDD and 771 HC) and 16 sites of REST-meta-MDD project show that the APO-GCN achieved a classification accuracy of 93.8%, outperforming those of the state-of-the-art classifier methods. The classification process is driven by multiple significant brain regions, and our method further reveals functional connectivity abnormalities between these brain regions, which are important biomarkers of classification. It is worth noting that the brain regions identified by the classifier and the networks involved are consistent with existing research results, which suggest that the pathogenesis of depression may be related to dysfunction of multiple brain networks.

PMID:39730018 | DOI:10.1016/j.neuroscience.2024.12.045

J Int Neuropsychol Soc. 2024 Dec 27:1-11. doi: 10.1017/S1355617724000717. Online ahead of print.

ABSTRACT

OBJECTIVE: Reading difficulties (RD) frequently co-occur with attention-deficit/hyperactivity disorder (ADHD), and children with both RD + ADHD often demonstrate greater challenges in reading and executive functions (EF) than those with RD-only.

METHODS: This study examined the effect of a 4-week EF-based reading intervention on behavioral and neurobiological correlates of EF among 8-12 y.o. English-speaking children with RD + ADHD (n = 19), RD-only (n = 18), and typically developing children (n = 18). Behavioral and resting-state fMRI data were collected from all participants before and after 4 weeks of the EF-based reading computerized program. Group (RD + ADHD, RD-only, typical readers) x Test (pre- and post-intervention) repeated measures ANOVAs were conducted for reading, EF, and brain functional connectivity (FC) measures.

RESULTS: Across groups, reading (fluency, comprehension) and EF (inhibition, speed of processing) behavioral performance improved following the intervention. Exploratory subgroup comparisons revealed that children with RD + ADHD, but not RD-only, showed significant gains in reading comprehension, whereas inhibition improved in both RD groups, but not among typical readers. Furthermore, across groups, FC between the frontoparietal (FP) and cingulo-opercular (CO) networks decreased following the intervention. Exploratory subgroup comparisons revealed that children with RD + ADHD, but not RD-only, showed a significant decrease in FC of FP-CO and FP-dorsal attention network.

CONCLUSIONS: These results support the differential response to an EF-based reading intervention of children with RD with and without comorbid ADHD at brain and behavioral levels.

PMID:39725652 | DOI:10.1017/S1355617724000717

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Dec 24:S2451-9022(24)00384-7. doi: 10.1016/j.bpsc.2024.12.008. Online ahead of print.

ABSTRACT

BACKGROUND: Prolonged Grief Disorder is a multidimensional condition with adverse health consequences. We hypothesized that enhanced negative emotional bias characterizes this disorder and underlies its key clinical symptoms.

METHODS: In a cross-sectional design, chronically grieving older adults (61.5±8.9 years old) experiencing probable Prolonged Grief Disorder (PGD; n=33) were compared with demographic- and time since loss-equated integrated (adaptive) grief participants (n=38). To probe generalized negative affective reactivity, participants performed an emotional face-matching task during fMRI scanning, and demographic and clinical assessments. Contrast maps (fearful + angry faces (-) shapes) were generated to determine group differences in brain activity within hypothesized affective and regulatory processing regions (amygdala, anterior insula, dorsal anterior cingulate, dorsolateral prefrontal cortex) and in exploratory whole-brain regression analyses.

RESULTS: The PGD group showed higher right amygdala activation to negative emotional stimuli, compared to the integrated grief group (pcorr<0.05), which positively correlated with intrusive thoughts. Generalized psychophysiological interaction analysis revealed lower task-dependent functional connectivity between the right amygdala and posterior cingulate cortex/precuneus in PGD (pcorr<0.05), which negatively correlated with avoidance of loss reminders. Resting-state functional connectivity between the identified right amygdala and thalamus was higher in PGD (pcorr<0.05), which negatively correlated with loneliness.

CONCLUSIONS: Dysregulated amygdala-centric neural activity and functional connectivity during processing of negative affective stimuli and at rest appear to differentiate prolonged from integrated grief in older adults. Future investigations using interventions to target amygdala-centric neural circuit abnormalities may provide new insights into the role of enhanced negative bias and related mechanisms underlying PGD and support treatment efficacy.

PMID:39725082 | DOI:10.1016/j.bpsc.2024.12.008

CNS Neurosci Ther. 2024 Dec;30(12):e70194. doi: 10.1111/cns.70194.

ABSTRACT

AIMS: The nucleus basalis of Meynert (NBM) is a major source of cholinergic innervation in the central nervous system. We aimed to investigate the characteristics of structural and functional alterations in the NBM and its projections in patients with mild cognitive impairment (MCI) and the effects of computerized cognitive training (CCT).

METHODS: Forty-five patients with MCI and 45 cognitively unimpaired controls (CUCs) were recruited. NBM volume, mean diffusivity (MD) of NBM white matter (WM) projections, and functional connectivity (FC) of projections of the NBM were measured with T1-weighted imaging, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-six MCI patients were randomly assigned to receive CCT or control training. The effects of CCT on the neuropsychological measures and MRI properties were analyzed with a linear mixed model (LMM).

RESULTS: We detected that compared with the CUCs, the MCI patients had a reduced volume of the NBM and a greater MD of both cholinergic pathways. Increased MD values of both pathways were related to lower scores of global cognition, processing speed and attention in all participants. After CCT intervention, significant group × timepoint effects on score of the Backward Digit Span and the FC between NBM and right putamen were observed in the CCT group compared to the control group.

CONCLUSION: NBM atrophy and WM pathway disruption occurred in MCI patients and were correlated with cognitive impairment. Working memory and the FC between NBM and right putamen could be improved by cognitive training.

PMID:39723443 | DOI:10.1111/cns.70194

Front Neurosci. 2024 Dec 11;18:1402657. doi: 10.3389/fnins.2024.1402657. eCollection 2024.

ABSTRACT

INTRODUCTION: Functional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.

METHODS: To address these challenges, we propose an interpretable fusion analytic framework to identify and understand ROI connectivity differences between two groups, revealing their distinctive features. The framework involves three steps: first, constructing ROI-based Functional Connectivity Networks (FCNs) to manage resting-state fMRI data; second, employing a Self-Attention Deep Learning Model (Self-Attn) for binary classification to generate attention distributions encoding group-level differences; and third, utilizing a Latent Space Item-Response Model (LSIRM) to extract group-representative ROI features, visualized on group summary FCNs.

RESULTS: We applied our framework to analyze four types of cognitive impairments, demonstrating their effectiveness in identifying significant ROIs that contribute to the differences between the two disease groups. The results reveal distinct connectivity patterns and unique ROI features, which differentiate cognitive impairments. Specifically, our framework highlighted group-specific differences in functional connectivity, validating its capability to capture meaningful insights from high-dimensional fMRI data.

DISCUSSION: Our novel interpretable fusion analytic framework addresses the challenges of analyzing high-dimensional, resting-state fMRI data. By integrating FCNs, a Self-Attention Deep Learning Model, and LSIRM, the framework provides an innovative approach to discovering ROI connectivity disparities between groups. The attention distribution and group-representative ROI features offer interpretable insights into brain activity patterns and their variations among cognitive impairment groups. This methodology has significant potential to enhance our understanding of cognitive impairments, paving the way for more targeted therapeutic interventions.

PMID:39723421 | PMC:PMC11668745 | DOI:10.3389/fnins.2024.1402657

Front Aging Neurosci. 2024 Dec 11;16:1421703. doi: 10.3389/fnagi.2024.1421703. eCollection 2024.

ABSTRACT

INTRODUCTION: Chemotherapy-related cognitive impairment (CRCI) remains poorly understood in terms of the mechanisms of cognitive decline. Neural hyperactivity has been reported on average in cancer survivors, but it is unclear which patients demonstrate this neurophenotype, limiting precision medicine in this population.

METHODS: We evaluated a retrospective sample of 80 breast cancer survivors and 80 non-cancer controls, aged 35-73, for which we had previously identified and validated three data-driven, biological subgroups (biotypes) of CRCI. We measured neural activity using the z-normalized percent amplitude of fluctuation from resting-state functional magnetic resonance imaging (MRI). We tested established, quantitative criteria to determine whether hyperactivity can accurately be considered compensatory. We also calculated the brain age gap by applying a previously validated algorithm to anatomic MRI.

RESULTS: We found that neural activity differed across the three CRCI biotypes and controls (F = 13.5, p < 0.001), with Biotype 2 demonstrating significant hyperactivity compared to the other groups (p < 0.004, corrected), primarily in prefrontal regions. Alternatively, Biotypes 1 and 3 demonstrated significant hypoactivity (p < 0.02, corrected). Hyperactivity in Biotype 2 met several of the criteria to be considered compensatory. However, we also found a positive relationship between neural activity and the brain age gap in these patients (r = 0.45, p = 0.042).

DISCUSSION: Our results indicated that neural hyperactivity is specific to a subgroup of breast cancer survivors and, while it seems to support preserved cognitive function, it could also increase the risk of accelerated brain aging. These findings could inform future neuromodulatory interventions with respect to the risks and benefits of upregulation or downregulation of neural activity.

PMID:39723153 | PMC:PMC11668692 | DOI:10.3389/fnagi.2024.1421703

CNS Neurosci Ther. 2024 Dec;30(12):e70184. doi: 10.1111/cns.70184.

ABSTRACT

AIMS: Cognitive functions are reduced in Parkinson's disease (PD) patients after deep brain stimulation (DBS) surgery. However, the underlying mechanisms remain unclear. The current study attempted to elucidate whether DBS alters the functional connectivity (FC) pattern of cognitive networks in PD patients.

METHODS: The study obtained fMRI and cognitive scale data from 37 PD patients before and after the DBS surgery. Seed-based FC analysis helped demonstrate the FC changes of the default mode network (DMN), executive control network (ECN), and dorsal attention network (DAN).

RESULTS: PD patients indicated significant network connectivity decline in DMN [such as in right precuneus, left angular gyrus, and left middle frontal gyrus (MFG)], ECN [such as in left inferior parietal gyrus, left MFG, and left supplementary motor area (SMA)], and DAN [such as in left inferior frontal gyrus and left MFG] post-DBS surgery. The phonemic fluency score was positively associated with the FC value of the right precuneus and left angular gyrus in DMN before DBS.

CONCLUSION: The general reduction in FC in the major cognitive networks after DBS surgery depicted the presence of the corresponding network reorganization. Further research can help explore the mechanism of impaired cognitive function post-DBS.

PMID:39722165 | DOI:10.1111/cns.70184

Acad Radiol. 2024 Dec 24:S1076-6332(24)00949-8. doi: 10.1016/j.acra.2024.12.001. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the most severe complications of systemic lupus erythematosus (SLE), and its early biomarkers and immune mechanisms remain unclear. This study utilizes Resting-State functional magnetic resonance imaging (rs-fMRI) to explore early neuroimaging biomarkers and potential immune mechanisms of brain injury in SLE, with a particular focus on anti-ribosomal P protein antibody (ARPA).

MATERIALS AND METHODS: A total of 47 SLE patients and 33 healthy controls (HCs) underwent rs-fMRI. Amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) values were compared between SLE and HC groups, and between ARPA-positive and ARPA-negative SLE patients. Correlation analyses were conducted to evaluate relationships between neuroimaging indicators and clinical indicators, including immunoglobulins and antiphospholipid antibodies. Conventional MRI findings, including white matter hyperintensities (WMHs), were also assessed.

RESULTS: SLE patients exhibited significant ALFF and DC alterations in regions associated with cognitive and sensory functions, including the inferior frontal and occipital regions. Notably, ARPA-positive SLE patients showed increased ALFF and DC values in areas related to cognitive and emotional regulation. Additionally, ACA-IgM and IgG correlate with brain injury in ARPA-positive patients. WMHs were more prevalent in ARPA-positive patients, with age and IgG levels identified as predictive markers for WMHs.

CONCLUSION: The combined use of ALFF and DC can effectively identify early biomarkers of brain injury in SLE patients. ARPA may synergize with other immune factors to combine to impair some brain functions, offering new insights into the immune-mediated mechanisms of SLE-related brain injury and potential targets for therapeutic interventions.

PMID:39721865 | DOI:10.1016/j.acra.2024.12.001

Sleep Med. 2024 Dec 18;126:239-247. doi: 10.1016/j.sleep.2024.12.025. Online ahead of print.

ABSTRACT

AIMS: The objective of the current study was to investigate the dynamic functional connectivity among large-scale brain networks in patients with insomnia, and to assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) treatment in these individuals.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data from 62 insomnia patients and 69 healthy controls were used to compare differences in dynamic functional connectivity between the two groups. A total of 26 insomnia patients underwent rTMS for four weeks. Changes in dynamic functional network connectivity was observed in insomnia patients following treatment. Additionally, the relationship between clinical symptoms and insomnia was analyzed using topological and correlation analyses.

RESULTS: Our findings demonstrated that insomnia patients exhibited a significantly lower fraction rate of negative connectivity between the dorsal default mode network (dDMN) and the visual network (VN) compared to healthy controls, while showing strong positive connectivity within the VN and the auditory network (AUN). It may be attributed to the restoration of normal dynamic functional connectivity between the dDMN and VN in insomnia patients following rTMS. Furthermore, the dynamic functional connectivity between the dDMN and VN was found to predict sleep quality and treatment outcome in insomnia patients.

CONCLUSION: Abnormal dynamic functional network connectivity between the dDMN and VN is a hallmark of insomnia, and may serve as a biomarker to assess the effects of rTMS treatment in insomnia patients.

PMID:39721360 | DOI:10.1016/j.sleep.2024.12.025

Hum Brain Mapp. 2024 Dec 15;45(18):e70111. doi: 10.1002/hbm.70111.

ABSTRACT

Learning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting-state functional connectivity is an effective method for detecting short-term brain alterations induced by various modality-based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty-four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting-state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

PMID:39720915 | DOI:10.1002/hbm.70111

J Pain Res. 2024 Dec 19;17:4433-4445. doi: 10.2147/JPR.S488988. eCollection 2024.

ABSTRACT

PURPOSE: The aim of the research was to observe the variations in brain activity between young cervical spondylosis patients with chronic neck pain (CNP) and healthy volunteers in the resting state and to investigate the central remodeling mechanisms in the patients.

PATIENTS AND METHODS: Our study recruited 31 patients with chronic neck pain from cervical spondylosis and 30 healthy volunteers. Eventually, 29 patients (CNP group) and 29 healthy volunteers (HC group) completed the acquisition of clinical data and resting-state functional magnetic resonance (rs BOLD-fMRI) amplitude of low-frequency fluctuations (ALFF) data; in addition, we assessed the relationship between differentially active brain regions and clinical indicators.

RESULTS: The CNP group found greater ALFF values in the insula, cingulate gyrus, prefrontal lobe, and other brain regions. The occipital, parietal, and other brain regions had lower ALFF values. In addition, there was a negative connection between the duration of the sickness in the CNP group and the ALFF value of the right superior parietal gyrus (SPG.R). The level of tenderness threshold exhibited a negative correlation with the ALFF value of the left insula (INS.L). In addition, the NPQ score showed a negative association with the ALFF value of the ORBinf.R and a positive correlation with the ALFF value of the CC1.L. Finally, the HADS-A score exhibited a positive correlation with the ALFF value of the right anterior cingulate and paracingulate gyrus (ACG.R).

CONCLUSION: Young patients with chronic neck pain show extensive central remodeling, with altered functional activity in pain-emotion brain areas (such as the cingulate gyrus and insula), pain-cognition brain areas (such as the prefrontal lobe), and other special sensory brain areas (such as the parietal and occipital lobes). These changes are linked to clinical tenderness, functional disability, and negative emotion indicators.

PMID:39720323 | PMC:PMC11668316 | DOI:10.2147/JPR.S488988

Front Behav Neurosci. 2024 Dec 10;18:1498612. doi: 10.3389/fnbeh.2024.1498612. eCollection 2024.

ABSTRACT

INTRODUCTION: Anxiety is an emotion necessary for human survival. However, persistent and excessive anxiety can be clinically challenging. Increased anxiety affects daily life and requires early detection and intervention. Therefore, a better understanding of the neural basis of mild anxiety is needed. However, previous studies have focused primarily on resting-state functional magnetic resonance imaging (rs-fMRI) in patients with psychiatric disorders presenting with anxiety. Notably, only a few studies have been conducted on healthy participants, and the relationship between anxiety and functional brain connectivity in the healthy range remains unclear. Therefore, in this study, we aimed to clarify the differences in functional brain connectivity at different degrees of anxiety among healthy participants.

METHODS: This study included 48 healthy participants with no history of psychiatric disorders. Participants were administered The General Health Questionnaire (GHQ) 60, a psychological test for assessing anxiety, and the Manifest Anxiety Scale (MAS). The participants then underwent rs-fMRI. Based on the results of each psychological test, the participants were classified into normal and anxiety groups, and the functional connectivity between the two groups was compared using a seed-to-voxel analysis.

RESULTS: Comparison of functional brain connectivity between the normal and anxiety groups classified based on the GHQ60 and MAS revealed differences between brain regions comprising the salience network (SN) in both psychological tests. For the GHQ60, the anxiety group showed reduced connectivity between the right supramarginal gyrus and insular cortex compared with the normal group. However, for the MAS, the anxiety group showed reduced connectivity between the right supramarginal and anterior cingulate cortical gyri compared with the normal group.

CONCLUSION: Functional connectivity within the SN was reduced in the group with higher anxiety when functional brain connectivity at different anxiety levels was examined in healthy participants. This suggests that anxiety is involved in changes in the functional brain connectivity associated with emotional processing and cognitive control.

PMID:39720304 | PMC:PMC11666370 | DOI:10.3389/fnbeh.2024.1498612

Neuroimage Clin. 2024 Dec 17;45:103727. doi: 10.1016/j.nicl.2024.103727. Online ahead of print.

ABSTRACT

Progressive supranuclear palsy (PSP) can present with different clinical variants which show distinct, but partially overlapping, patterns of neurodegeneration and tau deposition in a network of regions including cerebellar dentate, superior cerebellar peduncle, midbrain, thalamus, basal ganglia, and frontal lobe. We sought to determine whether disruptions in functional connectivity within this PSP network measured using resting-state functional MRI (rs-fMRI) differed between PSP-Richardson's syndrome (PSP-RS) and the cortical and subcortical clinical variants of PSP. Structural MRI and rs-fMRI scans were collected for 36 PSP-RS, 25 PSP-cortical and 34 PSP-subcortical participants who met the Movement Disorder Society PSP clinical criteria. Ninety participants underwent flortaucipir-PET scans. MRIs were processed using CONN Toolbox. Functional connectivity between regions of the PSP network was compared between each PSP group and 83 healthy controls, and between the PSP groups, covarying for age. The effect of flortaucipir uptake and clinical scores on connectivity was assessed. Connectivity was reduced in PSP-RS compared to controls throughout the network, involving cerebellar dentate, midbrain, basal ganglia, thalamus, and frontal regions. Frontal regions showed reduced connectivity to other regions in the network in PSP-cortical, particularly the thalamus, caudate and substantia nigra. Disruptions in connectivity in PSP-subcortical were less pronounced, with the strongest disruption between the pallidum and striatum. There was moderate evidence that elevated subcortical flortaucipir uptake correlated with both increased and reduced connectivity between regions of the PSP network. Lower connectivity within the PSP network correlated with worse performance on clinical tests, including PSP rating scale. Patterns of disrupted functional connectivity revealed both variant-specific and shared disease pathways within the PSP network among PSP clinical variants, providing insight into disease heterogeneity.

PMID:39719808 | DOI:10.1016/j.nicl.2024.103727

Radiol Med. 2024 Dec 24. doi: 10.1007/s11547-024-01938-0. Online ahead of print.

ABSTRACT

The gut microbiota emerged as a potential modulator of brain connectivity in health and disease. This systematic review details current evidence on the gut-brain axis and its influence on brain connectivity. The initial set of studies included 532 papers, updated to January 2024. Studies were selected based on employed techniques. We excluded reviews, studies without connectivity focus, studies on non-human subjects. Forty-nine papers were selected. Employed techniques in healthy subjects included 15 functional magnetic resonance imaging studies (fMRI), 5 diffusion tensor imaging, (DTI) 1 electroencephalography (EEG), 6 structural magnetic resonance imaging, 2 magnetoencephalography, 1 spectroscopy, 2 arterial spin labeling (ASL); in patients 17 fMRI, 6 DTI, 2 EEG, 9 structural MRI, 1 transcranial magnetic stimulation, 1 spectroscopy, 2 R2*MRI. In healthy subjects, the gut microbiota was associated with connectivity of areas implied in cognition, memory, attention and emotions. Among the tested areas, amygdala and temporal cortex showed functional and structural differences based on bacteria abundance, as well as frontal and somatosensory cortices, especially in patients with inflammatory bowel syndrome. Several studies confirmed the connection between microbiota and brain functions in healthy subjects and patients affected by gastrointestinal to renal and psychiatric diseases.

PMID:39718685 | DOI:10.1007/s11547-024-01938-0

Cereb Cortex. 2024 Dec 3;34(12):bhae489. doi: 10.1093/cercor/bhae489.

ABSTRACT

Neural oscillations are fundamental for brain function and govern various cognitive processes. Recent functional magnetic resonance imaging advances offer the opportunity to study frequency-specific properties of blood-oxygen-level-dependent oscillations at multiple frequency bands. However, most have focused on spontaneous brain activity in the resting state, leaving a gap in direct evidence regarding the specific activations of cognitive tasks across different frequency bands. We aim to address this gap by exploring the role of blood-oxygen-level-dependent oscillations across multiple frequency bands in cognitive processes. We used task-functional magnetic resonance imaging data of 339 healthy young adults from the Human Connectome Project to map the activation patterns of performing seven cognitive tasks at multiple frequency bands (ie slow-1 to slow-6). Our findings revealed that different frequency bands are associated with distinct task-activation patterns. Specifically, slow-1/2/3 oscillations primarily contribute to local sensory information processing, while slow-4 is crucial for various fundamental cognitive functions. Slow-5 is involved in cognitive processes that require greater memory load, integrated cognitive processing, and attention maintenance. This underscores the importance of analyzing a broad frequency range to capture the full spectrum of cognitive function, highlighting the diverse roles of different frequency bands in brain activity, shedding light on the underlying mechanism of brain-behavior associations.

PMID:39716742 | DOI:10.1093/cercor/bhae489

Neuroimage. 2024 Dec 21:120967. doi: 10.1016/j.neuroimage.2024.120967. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.

PMID:39716522 | DOI:10.1016/j.neuroimage.2024.120967

PLoS Comput Biol. 2024 Dec 23;20(12):e1012692. doi: 10.1371/journal.pcbi.1012692. Online ahead of print.

ABSTRACT

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

PMID:39715231 | DOI:10.1371/journal.pcbi.1012692

Front Neuroimaging. 2024 Dec 6;3:1473399. doi: 10.3389/fnimg.2024.1473399. eCollection 2024.

ABSTRACT

INTRODUCTION: Adjustable lifestyle factors, such as diet, are associated with cognitive functions, structural and functional brain measures, but the association between the functional connectivity (FC) and the Mediterranean Diet (Medicine) in population without dementia is yet to be explored.

METHODS: The association between MeDi and brain FC in 105 individuals without dementia aged 63 (SD ± 8.72) years old who underwent brain MRI including resting-state (rs) functional MRI (fMRI) was examined. Dietary intake was evaluated through four 24-h recalls using the multiple-pass method and adherence to the MeDi was estimated using the MedDietScore, with higher values indicating greater adherence to MeDi. Multivariable linear regression models were used to investigate the associations between FC (both positive and negative associations) and MedDietScore.

RESULTS: Rs-fMRI analysis revealed significant associations between FC and MedDietScore. The FC between the medial prefrontal cortex and a cluster located in left postcentral gyrus and in the left supramarginal gyrus was positively associated with MedDietScore. On the other hand, the FC between medial visual and right posterior division of both middle and superior temporal gyrus was negatively associated with MedDietScore. Of note, a temporal negative correlation was detected between above-mentioned FC networks. The FC between superior temporal gyrus and occipital regions was associated with participants' attention, executive functions, and memory scores. Furthermore, the associations for attention and executive functions were pronounced in participants with high adherence to MeDi compared to those with low adherence to MeDi.

DISCUSSION: In conclusion, our study documented an association between higher adherence to MeDi and rs-FC in fronto-parietal and temporo-occipital regions, particularly in areas that are involved in cognitive processes altered across normal and pathological aging. From a clinical point of view, our findings support a favorable role of MeDi on FC which may have significant clinical implications in the rapidly aging population. Rs-fMRI is also proposed as a useful tool in the emerging field of nutritional neuroscience and a candidate non-invasive biomarker of brain aging.

PMID:39713787 | PMC:PMC11659224 | DOI:10.3389/fnimg.2024.1473399

bioRxiv [Preprint]. 2024 Dec 10:2024.12.05.626723. doi: 10.1101/2024.12.05.626723.

ABSTRACT

Cerebral glucose metabolism (CMRGlc) systematically decreases with advancing age. We sought to identify correlates of decreased CMRGlc in the spectral properties of fMRI signals imaged in the task-free state. We analyzed lifespan resting-state fMRI data acquired in 455 healthy adults (ages 18-87 years) and cerebral metabolic data acquired in a separate cohort of 94 healthy adults (ages 25-45 years, 65-85 years). We characterized the spectral properties of the fMRI data in terms of the relative predominance of slow vs. fast activity using the spectral slope (SS) measure. We found that the relative proportion of fast activity increases with advancing age (SS flattening) across most cortical regions. The regional distribution of spectral slope was topographically correlated with CMRGlc in young adults. Notably, whereas most older adults maintained a youthful pattern of SS topography, a distinct subset of older adults significantly diverged from the youthful pattern. This subset of older adults also diverged from the youthful pattern of CMRGlc metabolism. This divergent pattern was associated with T2-weighted signal changes in frontal lobe white matter, an independent marker of small vessel disease. These findings suggest that BOLD signal spectral slope flattening may represent a biomarker of age-associated neurometabolic pathology.

PMID:39713346 | PMC:PMC11661102 | DOI:10.1101/2024.12.05.626723

Cogn Neurodyn. 2024 Dec;18(6):3741-3756. doi: 10.1007/s11571-024-10139-4. Epub 2024 Aug 12.

ABSTRACT

Altered connectivity within complex functional networks has been observed in individuals with post-stroke cognitive impairment (PSCI) and during cognitive tasks. This study aimed to identify a cognitive function network that is responsive to cognitive changes during cognitive tasks and also sensitive to PSCI. To explore the network, we analyzed resting-state fMRI data from 20 PSCI patients and task-state fMRI data from 100 unrelated healthy young adults using functional connectivity analysis. We further employed spectral dynamic causal modeling to examine the effective connectivity among the pivotal regions within the network. Our findings revealed a common cognitive network that encompassed the hub regions 231 in the Subcortical network (SC), 70, 199, 242 in the Frontoparietal network (FP), 214 in the Visual II network, and 253 in the Cerebellum network (CBL). These hubs' effective connectivity, which showed reliable but slight changes during different cognitive tasks, exhibited notable alterations when comparing post-stroke cognitive impairment and improvement statuses. Decreased coupling strengths were observed in effective connections to CBL253 and from SC231 and FP70 in the improvement status. Increased connections to SC231 and FP70, from CBL253 and FP242, as well as from FP199 and FP242 to FP242 were observed in this status. These alterations exhibited a high sensitivity to signs of recovery, ranging from 80 to 100%. The effective connectivity pattern in both post-stroke cognitive statuses also reflected the influence of the MoCA score. This research succeeded in identifying a cognitive network with sensitive effective connectivity to cognitive changes after stroke, presenting a potential neuroimaging biomarker for forthcoming interventional studies.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10139-4.

PMID:39712115 | PMC:PMC11655769 | DOI:10.1007/s11571-024-10139-4