Most recent paper

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

Cogn Neurodyn. 2024 Dec;18(6):3839-3846. doi: 10.1007/s11571-024-10166-1. Epub 2024 Aug 29.

ABSTRACT

There is, at present, a lack of consensus regarding precisely what is meant by the term 'energy' across the sub-disciplines of neuroscience. Definitions range from deficits in the rate of glucose metabolism in consciousness research to regional changes in neuronal activity in cognitive neuroscience. In computational neuroscience virtually all models define the energy of neuronal regions as a quantity that is in a continual process of dissipation to its surroundings. This, however, is at odds with the definition of energy used across all sub-disciplines of physics: a quantity that does not change as a dynamical system evolves in time. Here, we bridge this gap between the dissipative models used in computational neuroscience and the energy-conserving models of physics using a mathematical technique first proposed in the context of fluid dynamics. We go on to derive an expression for the energy of the linear time-invariant (LTI) state space equation. We then use resting-state fMRI data obtained from the human connectome project to show that LTI energy is associated with glucose uptake metabolism. Our hope is that this work paves the way for an increased understanding of energy in the brain, from both a theoretical as well as an experimental perspective.

PMID:39712109 | PMC:PMC11655998 | DOI:10.1007/s11571-024-10166-1

Cogn Neurodyn. 2024 Dec;18(6):3585-3601. doi: 10.1007/s11571-024-10176-z. Epub 2024 Sep 13.

ABSTRACT

Autism Spectrum Disorder(ASD) is a type of neurological disorder that is common among children. The diagnosis of this disorder at an early stage is the key to reducing its effects. The major symptoms include anxiety, lack of communication, and less social interaction. This paper presents a systematic review conducted based on PRISMA guidelines for automated diagnosis of ASD. With rapid development in the field of Data Science, numerous methods have been proposed that can diagnose the disease at an early stage which can minimize the effects of the disorder. Machine learning and deep learning have proven suitable techniques for the automated diagnosis of ASD. These models have been developed on various datasets such as ABIDE I and ABIDE II, a frequently used dataset based on rs-fMRI images. Approximately 26 articles have been reviewed after the screening process. The paper highlights a comparison between different algorithms used and their accuracy as well. It was observed that most researchers used DL algorithms to develop the ASD detection model. Different accuracies were recorded with a maximum accuracy close to 0.99. Recommendations for future work have also been discussed in a later section. This analysis derived a conclusion that AI-emerged DL and ML technologies can diagnose ASD through rs-fMRI images with maximum accuracy. The comparative analysis has been included to show the accuracy range.

PMID:39712105 | PMC:PMC11656001 | DOI:10.1007/s11571-024-10176-z

Alzheimers Dement. 2024 Dec 23. doi: 10.1002/alz.14349. Online ahead of print.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD) is characterized by the presence of two proteinopathies, amyloid and tau, which have a cascading effect on the functional and structural organization of the brain.

METHODS: In this study, we used a supervised machine learning technique to build a model of functional connections that predicts cerebrospinal fluid (CSF) p-tau/Aβ42 (the PATH-fc model). Resting-state functional magnetic resonance imaging (fMRI) data from 289 older adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were utilized for this model.

RESULTS: We successfully derived the PATH-fc model to predict the ratio of p-tau/Aβ42 as well as cognitive functioning in older adults across the spectrum of healthy and pathological aging. However, the in-sample fit magnitude was low, indicating a need for further model development.

DISCUSSION: Our pathology-based model of functional connectivity included representation from multiple canonical networks of the brain with intra-network connectivity associated with low pathology and inter-network connectivity associated with higher levels of pathology.

HIGHLIGHTS: Whole-brain functional connectivity model (PATH-fc) is linked to AD pathophysiology. The PATH-fc model predicts performance in multiple domains of cognitive functioning. The PATH-fc model is a distributed model including representation from all canonical networks.

PMID:39711458 | DOI:10.1002/alz.14349

Hippocampus. 2025 Jan;35(1):e23678. doi: 10.1002/hipo.23678.

ABSTRACT

Although the medial temporal lobe (MTL) is traditionally considered a region dedicated to long-term memory, recent neuroimaging and intracranial recording evidence suggests that the MTL also contributes to certain aspects of visual short-term memory (VSTM), such as the quality or precision of retained VSTM content. This study aims to further investigate the MTL's role in VSTM precision through the application of transcranial direct current stimulation (tDCS) and functional magnetic resonance imaging (fMRI). Participants underwent 1.5 mA offline tDCS over bilateral temporal lobes using left cathodal and right anodal electrodes, administered for either 20 min (active) or 0.5 min within a 20-min window (sham), in a counterbalanced design. As the electrical current passes through midbrain structures with this bilateral stimulation montage, prior behavioral and modeling evidence suggests that this tDCS protocol can modulate MTL functions. To confirm this and examine its impacts on VSTM, participants completed a VSTM color recall task immediately following tDCS, while undergoing a 20-min fMRI scan and a subsequent 7.5-min resting-state scan, during which they focused on a fixation cross. Behavioral results indicated that this tDCS protocol decreased VSTM precision without significantly affecting overall recall success. Furthermore, psychophysiological interaction analysis revealed that tDCS over the temporal lobe modulated hippocampal-occipital functional connectivity during the VSTM task, despite no main effect on fMRI BOLD activity. Notably, this modulation was also observed during resting-state fMRI 15-20 min post-tDCS, with the magnitude of the effect correlating with participants' behavioral changes in VSTM precision across active and control conditions. Combined, these findings suggest that tDCS over the temporal lobe can modulate the intrinsic functional connectivity between the MTL and visual sensory areas, thereby affecting VSTM precision.

PMID:39711102 | DOI:10.1002/hipo.23678

Magn Reson Imaging. 2024 Dec 20:110296. doi: 10.1016/j.mri.2024.110296. Online ahead of print.

ABSTRACT

INTRODUCTION: More and more evidence suggesting that internal carotid artery stenosis is not only a risk factor for ischemic stroke but also for cognitive impairments. Hypoperfusion and silent micro emboli have been reported as the pathophysiological mechanisms causing cognitive impairment. The effect of carotid artery stenting (CAS) on cognitive function varied from study to study. This study aims to explore the effect of CAS on cognition and exam the changes in cerebral perfusion and brain connectivity with pulsed arterial spin labeling (pASL) and resting-state functional MRI (R-fMRI).

METHODS: We conducted a controlled trial to assess alterations in cognitive performance among patients with "asymptomatic" carotid artery stenosis prior to and 3 months post-CAS intervention. Cognitive function including the Montreal Cognitive Assessment (MoCA) Beijing Version, the Minimum Mental State Examination (MMSE), the Digit Symbol Test, the Rey Auditory Verbal Learning Test (RAVLT), and the Verbal Memory Test. pASL perfusion MRI and R-fMRI were also performed prior to and 3 months post-CAS intervention.

RESULTS: 13 patients completed all the follow-up. We observed increased perfusion in the right parietal lobe and right occipital lobe, increased amplitude of low-frequency fluctuation (ALFF) in the right precentral gyrus, increased connectivity to the posterior cingulate cortex (PCC) in the right frontal gyrus and right precuneus, and increased voxel-wise mirrored homotopic connectivity (VMHC) in the right precuneus 3 months after CAS when compared with prior to CAS. Cognitive test results showed significant improvement in the scores on the MMSE, the Verbal Memory test, and the delayed recall.

CONCLUSION: CAS can partly improve the cognitive function in patients with "asymptomatic" carotid artery stenosis, and the improvement may be attributable to the increased perfusion in the right parietal lobe and right occipital lobe, increased ALFF in the right precentral gyrus, increased connectivity to the PCC in the right frontal gyrus and right precuneus, and increased VMHC in the right precuneus.

PMID:39710010 | DOI:10.1016/j.mri.2024.110296

Psychiatry Res Neuroimaging. 2024 Dec 18;347:111943. doi: 10.1016/j.pscychresns.2024.111943. Online ahead of print.

ABSTRACT

Resting-state functional connectivity (rsFC) measures have enjoyed significant success in discovering the neuropathological characteristics of schizophrenia (SZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). However, it is unknown whether and how the spatial and temporal coupling relationships across rsFC measures would be altered in these psychiatric disorders. Here, resting-state fMRI data were obtained from a transdiagnostic sample of healthy controls (HC) and individuals with SZ, BD, and ADHD. We used Kendall's W to compute volume-wise and voxel-wise concordance across rsFC measures, followed by group comparisons. In terms of the spatial coupling, both SZ and BD individuals exhibited decreased volume-wise concordance compared with HC. Regarding the temporal coupling, SZ individuals showed decreased voxel-wise concordance in the right lateral occipital cortex relative to HC. BD individuals exhibited decreased voxel-wise concordance in the bilateral basal forebrain and bilateral superior/middle temporal gyrus compared to HC. Additionally, correlation analyses demonstrated positive associations of voxel-wise concordance in the left basal forebrain with negative symptoms including alogia and affective flattening in pooled SZ and BD individuals. Our findings of distinct patterns of spatial and temporal decoupling across rsFC measures among SZ, BD, and ADHD may provide unique insights into the neuropathological mechanisms of these psychiatric disorders.

PMID:39709676 | DOI:10.1016/j.pscychresns.2024.111943

Med Image Anal. 2024 Dec 16;101:103433. doi: 10.1016/j.media.2024.103433. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence. Our GitHub implementation is publicly available on https://github.com/bishalth01/BrainRGIN/.

PMID:39708510 | DOI:10.1016/j.media.2024.103433

Chin J Integr Med. 2024 Dec 21. doi: 10.1007/s11655-024-4205-7. Online ahead of print.

ABSTRACT

OBJECTIVE: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales.

RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001).

CONCLUSIONS: Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).

PMID:39707137 | DOI:10.1007/s11655-024-4205-7

Front Aging Neurosci. 2024 Dec 5;16:1483449. doi: 10.3389/fnagi.2024.1483449. eCollection 2024.

ABSTRACT

INTRODUCTION: We investigated the neural correlates of cognitive decline in visual word perception from the perspective of intrinsic brain networks.

METHODS: A total of 19 healthy older adults and 22 young adults were recruited to participate in two functional magnetic resonance imaging (fMRI) sessions (one resting-state session and one for localizer tasks), along with a visual word perceptual processing task. We examined age-related alterations in resting-state functional connectivity (FC) within the word network, as well as between the word network and other networks. We tested their associations with behavioral performance in word and symbol-form processing.

RESULTS: We found that, compared to young adults, older adults exhibited increased FC between the two word-selective regions in the left and right ventral occipitotemporal cortex (vOT). Additionally, older adults exhibited increased FC between these two word-selective regions and non-word-selective regions. Notably, these FC alterations correlated with individual differences in behavioral performance in visual word perception.

DISCUSSION: These results suggest that cognitive decline in visual word perception is associated with decreased segregation within and beyond the word network in the aging brain. Our findings support the neural dedifferentiation hypothesis for cognitive decline in visual word processing and improve our understanding of interactive neural specialization theory.

PMID:39703923 | PMC:PMC11655501 | DOI:10.3389/fnagi.2024.1483449