fMRI Research Articles

Plasma p-tau181 and GFAP reflect 7T MR-derived changes in Alzheimer's disease: A longitudinal study of structural and functional MRI and MRS.

Associations between longitudinal changes of plasma biomarkers and cerebral magnetic resonance (MR)-derived measurements in Alzheimer's disease (AD) remain unclear. In a study population (n=127) of healthy older adults and patients within the AD continuum, we examined associations between longitudinal plasma amyloid beta 42/40 ratio, tau phosphorylated at threonine 181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and 7T structural and functional MR imaging and spectroscopy using linear mixed models. Increases in both p-tau181 and GFAP showed the strongest associations to 7T MR-derived measurements, particularly with decreasing parietal cortical thickness, decreasing connectivity of the salience network, and increasing neuroinflammation as determined by MR spectroscopy (MRS) myo-inositol. Both plasma p-tau181 and GFAP appear to reflect disease progression, as indicated by 7T MR-derived brain changes which are not limited to areas known to be affected by tau pathology and neuroinflammation measured by MRS myo-inositol, respectively. This study leverages high-resolution 7T magnetic resonance (MR) imaging and MR spectroscopy (MRS) for Alzheimer's disease (AD) plasma biomarker insights. Tau phosphorylated at threonine 181 (p-tau181) and glial fibrillary acidic protein (GFAP) showed the largest changes over time, particularly in the AD group. p-tau181 and GFAP are robust in reflecting 7T MR-based changes in AD. The strongest associations were for frontal/parietal MR changes and MRS neuroinflammation.

Worry and rumination elicit similar neural representations: neuroimaging evidence for repetitive negative thinking.

Repetitive negative thinking (RNT) captures shared cognitive and emotional features of content-specific cognition, including future-focused worry and past-focused rumination. The degree to which these distinct but related processes recruit overlapping neural structures is undetermined, because most neuroscientific studies only examine worry or rumination in isolation. To address this, we developed a paradigm to elicit idiographic worries and ruminations during an fMRI scan in 39 young adults with a range of trait RNT scores. We measured concurrent emotion ratings and heart rate as a physiological metric of arousal. Multivariate representational similarity analysis revealed that regions distributed across default mode, salience, and frontoparietal control networks encode worry and rumination similarly. Moreover, heart rate did not differ between worry and rumination. Capturing the shared neural features between worry and rumination throughout networks supporting self-referential processing, memory, salience detection, and cognitive control provides novel empirical evidence to bolster cognitive and clinical models of RNT.

Neuroscience Studies on ADHD and Autism: Exploring Neurodevelopmental Mechanisms and Clinical Implications

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two prevalent neurodevelopmental conditions characterized by distinct yet overlapping cognitive, behavioral, and neurological profiles. Neuroscience research has provided profound insights into the underlying neurodevelopmental mechanisms that contribute to these conditions, offering pathways for improved diagnosis, treatment, and support. This study explores the neural correlates of ADHD and ASD, focusing on brain structure, functional connectivity, and neurotransmitter systems. It also investigates genetic and environmental influences that contribute to these disorders. By integrating cutting-edge neuroimaging techniques such as functional MRI (fMRI) and electroencephalography (EEG) with behavioral assessments, this research aims to identify biomarkers that enhance diagnostic precision and therapeutic interventions. Additionally, it examines the implications of neuroplasticity and early intervention strategies in mitigating symptom severity. The study emphasizes the importance of multidisciplinary approaches that bridge neuroscience, psychology, and clinical practice to improve outcomes for individuals with ADHD and ASD. By providing a deeper understanding of the neurodevelopmental underpinnings of these conditions, this research contributes to a growing body of knowledge aimed at fostering inclusive and evidence-based care.

Challenges in the measurement and interpretation of dynamic functional connectivity

Abstract In functional MRI (fMRI), dynamic functional connectivity (dFC) typically refers to fluctuations in measured functional connectivity on a time scale of seconds. This perspective piece focuses on challenges in the measurement and interpretation of functional connectivity dynamics. Sampling error, physiological artifacts, arousal level, and task state all contribute to variability in observed functional connectivity. In our view, the central challenge in the interpretation of functional connectivity dynamics is distinguishing between these sources of variability. We believe that applications of functional connectivity dynamics to track spontaneous cognition or as a biomarker of neuropsychiatric conditions must contend with these statistical issues as well as interpretative complications. In this perspective, we include a systematic survey of the recent literature, in which sliding window analysis remains the dominant methodology (79%). We identify limitations with this approach and discuss strategies for improving the analysis and interpretation of sliding window dFC by considering the time scale of measurement and appropriate experimental controls. We also highlight avenues of investigation that could help the field to move forward.

Trajectories of human brain functional connectome maturation across the birth transition.

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

Resting state functional connectivity in adolescents with substance use disorder and their unaffected siblings

We aimed to examine resting-state functional connectivity (rsFC) in adolescents with substance use disorder (SUD) and their unaffected biological siblings (SIB), relative to typically-developing controls (TDC) in order to identify alterations in functional network organization that may be associated with the familial risk for SUD. Resting-state functional magnetic resonance imaging analysis included 20 adolescents with SUD, 20 SIB, and 18 TDC. Network‐based analysis revealed that adolescents with SUD had significantly both weaker and higher rsFC compared to TDC mainly within the default-mode network (DMN) and between the DMN, fronto-parietal (FPN) and salience networks. In addition, adolescents with SUD showed lower rsFC between the visual network and other functional networks. Although the SIB group did not differ from TDC in the whole brain analysis, they showed lower rsFC within DMN and also between the visual network and other large-scale networks as well as higher rsFC between DMN and FPN compared to TDC in connections found to be abnormal in SUD group. Our results suggest that lower rsFC within DMN and higher rsFC between the DMN with FPN which were evident both in SUD and in SIB groups, and might be related to the familial predisposition for SUD.

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC). AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents. METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD. RESULTS Compared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%. CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

Using dynamic graph convolutional network to identify individuals with major depression disorder

Objective and quantitative neuroimaging biomarkers are crucial for early diagnosis of major depressive disorder (MDD). However, previous studies using machine learning (ML) to distinguish MDD have often used small sample sizes and overlooked MDD's neural connectome and mechanism. To address these gaps, we applied Dynamic Graph Convolutional Nets (DGCNs) to a large multi-site dataset of 2317 resting state functional MRI (RS-fMRI) scans from 1081 MDD patients and 1236 healthy controls from 16 Rest-meta-MDD consortium sites. Our DGCN model combined with the personal whole brain functional connectivity (FC) network achieved an accuracy of 82.5 % (95 % CI:81.6–83.4 %, AUC:0.869), outperforming other universal ML classifiers. The most prominent domains for classification were mainly in the default mode network, fronto-parietal and cingulo-opercular network. Our study supports the stability and efficacy of using DGCN to characterize MDD and demonstrates its potential in enhancing neurobiological comprehension of MDD by detecting clinically related disorders in FC network topologies.

Altered Neural Processing of Interoception in Patients With Functional Neurological Disorder: A Task-Based fMRI Study.

Research suggests that disrupted interoception contributes to the development and maintenance of functional neurological disorder (FND); however, no functional neuroimaging studies have examined the processing of interoceptive signals in patients with FND. The authors examined univariate and multivariate functional MRI neural responses of 38 patients with mixed FND and 38 healthy control individuals (HCs) during a task exploring goal-directed attention to cardiac interoception-versus-control (exteroception or rest) conditions. The relationships between interoception-related neural responses, heartbeat-counting accuracy, and interoceptive trait prediction error (ITPE) were also investigated for FND patients. When attention was directed to heartbeat signals versus exteroception or rest tasks, FND patients showed decreased neural activations (and reduced coactivations) in the right anterior insula and bilateral dorsal anterior cingulate cortices (among other areas), compared with HCs. For FND patients, heartbeat-counting accuracy was positively correlated with right anterior insula and ventromedial prefrontal activations during interoception versus rest. Cardiac interoceptive accuracy was also correlated with bilateral dorsal anterior cingulate activations in the interoception-versus-exteroception contrast, and neural activations were correlated with ITPE scores, showing inverse relationships to those observed for heartbeat-counting accuracy. This study identified state and trait interoceptive disruptions in FND patients. Convergent between- and within-group findings contextualize the pathophysiological role of cingulo-insular (salience network) areas across the spectrum of functional seizures and functional movement disorder. These findings provide a starting point for the future development of comprehensive neurophysiological assessments of interoception for FND patients, features that also warrant research as potential prognostic and monitoring biomarkers.

Comparative effects of topiramate and naltrexone on neural activity during anticipatory anxiety in individuals with alcohol use disorder.

Topiramate has been found to be effective in reducing alcohol use and may also attenuate anxiety severity in patients with alcohol use disorder (AUD). This study compared the neural response of treatment-seeking patients with AUD on either topiramate or naltrexone during an anticipatory anxiety task. Participants were 42 patients with AUD who were randomized to receive either topiramate (n =23; titrated dose up to 200mg/day) or naltrexone (n = 19; 50mg/day) for 12-weeks as part of a larger randomized controlled trial. Following 6weeks of treatment, participants completed an anticipatory anxiety task during a functional magnetic resonance imaging (fMRI) session. The task presented a series of high-threat and low-threat stimuli followed by an unpleasant or pleasant image, respectively. Primary whole-brain analyses revealed no significant differences in neural activation between the topiramate and naltrexone groups. Deactivation for safe cues relative to threat cues was observed within the precuneus, inferior parietal lobule and the cingulate gyrus. In the precentral and middle frontal gyri, threat cues elicited greater activation. Exploratory analyses revealed an effect of change in anxiety from baseline to week 6, with a greater reduction associated with a reduced response to threat cues relative to safe cues in the cuneus and lingual gyrus. The current study is the first to examine and compare neural activation during anticipatory anxiety in treatment-seeking individuals on topiramate and naltrexone. This preliminary research contributes to our understanding of the therapeutic mechanisms of these alcohol pharmacotherapies.

Quantitative mapping of cerebrovascular reactivity amplitude and delay with breath-hold BOLD fMRI when end-tidal CO 2 quality is low.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in order to regulate blood flow, is a clinically useful measure of cerebrovascular health. CVR is often measured using a breath-hold task to modulate blood CO 2 levels during an fMRI scan. Measuring end-tidal CO 2 (P ET CO 2 ) with a nasal cannula during the task allows CVR amplitude to be calculated in standard units (vascular response per unit change in CO 2 , or %BOLD/mmHg) and CVR delay to be calculated in seconds. The use of standard units allows for normative CVR ranges to be established and for CVR comparisons to be made across subjects and scan sessions. Although breath-holding can be successfully performed by diverse patient populations, obtaining accurate P ET CO 2 measurements requires additional task compliance; specifically, participants must breathe exclusively through their nose and exhale immediately before and after each breath hold. Meeting these requirements is challenging, even in healthy participants, and this has limited the translational potential of breath-hold fMRI for CVR mapping. Previous work has focused on using alternative regressors such as respiration volume per time (RVT), derived from respiratory belt measurements, to map CVR. Because measuring RVT does not require additional task compliance from participants, it is a more feasible measure than P ET CO 2. However, using RVT does not produce CVR in standard units. In this work, we explored how to achieve CVR maps, in standard units, when breath-hold task P ET CO 2 data quality is low. First, we evaluated whether RVT could be scaled to units of mmHg using a subset of P ET CO 2 data of sufficiently high quality. Second, we explored whether a P ET CO 2 timeseries predicted from RVT using deep learning allows for more accurate CVR measurements. Using a dense-mapping breath-hold fMRI dataset, we showed that both rescaled RVT and rescaled, predicted P ET CO 2 can be used to produce maps of CVR amplitude and delay in standard units with strong absolute agreement to ground-truth maps. However, the rescaled, predicted P ET CO 2 regressor resulted in superior accuracy for both CVR amplitude and delay. In an individual with regions of increased CVR delay due to Moyamoya disease, the predicted P ET CO 2 regressor also provided greater sensitivity to pathology than RVT. Ultimately, this work will increase the clinical applicability of CVR in populations exhibiting decreased task compliance.

Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum

Abstract Amyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer’s disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown Aβ deposition signatures.

APPLICATION OF THE ITEM RESPONSE THEORY IN IMAGE PROCESSING

The Item Response Theory (IRT) was applied to image processing where the item's difficulty parameter was used to reconstruct a region of interest in an image contaminated with noise. Shannon's entropy was used for the dichotomization of the image pixels. To demonstrate the efficiency of the proposed method, it was used on simulated data that can be related to functional magnetic resonance imaging (fMRI). The results on simulated data showed that IRT achieved a high percentage of accuracy in identifying the region of interest in the image and that, on average, the estimation converges to the true region of interest, thus proving to be a promising method for the analysis of such data.

In vivo assessment of pediatric kidney function using multi-parametric and multi-nuclear functional magnetic resonance imaging: challenges, perspectives, and clinical applications

AbstractThe conventional methods for assessing kidney function, such as glomerular filtration rate and microalbuminuria, provide only partial insight into kidney function. Multi-parametric and multi-nuclear functional resonance magnetic imaging (MRI) techniques are innovative approaches to unraveling kidney physiology. Multi-parametric MRI includes various sequences to evaluate kidney perfusion, tissue oxygenation, and microstructure characterization, including fibrosis—a key pathological event in acute and chronic kidney disease and in transplant patients—without the need for invasive kidney biopsy. Multi-nuclear MRI detects nuclei other than protons. 23Na MRI enables visualization of the corticomedullary gradient and assessment of tissue sodium storage, which can be particularly relevant for personalized medicine in salt-wasting tubular disorders. Meanwhile, 31P-MRS measures intracellular phosphate and ATP variations, providing insights into oxidative metabolism in the muscle during exercise and recovery. This technique can be useful for detecting subclinical ischemia in chronic kidney disease and in tubulopathies with kidney phosphate wasting. These techniques are non-invasive and do not involve radiation exposure, making them especially suitable for longitudinal and serial assessments. They enable in vivo evaluation of kidney function on a whole-organ basis within a short acquisition time and with the ability to distinguish between medullary and cortical compartments. Therefore, they offer considerable potential for pediatric patients. In this review, we provide a brief overview of the main imaging techniques, summarize available literature data on both adult and pediatric populations, and examine the perspectives and challenges associated with multi-parametric and multi-nuclear MRI. Graphical Abstract

Neural correlates associated with a family history of alcohol use disorder: A narrative review of recent findings

AbstractA family history of alcohol use disorder (AUD) is associated with a significantly increased risk of developing AUD in one's lifetime. The previously reviewed literature suggests there are structural and functional neurobiological markers associated with familial AUD, but to our knowledge, no recent review has synthesized the latest findings across neuroimaging studies in this at‐risk population. For this narrative review, we conducted keyword searches in electronic databases to find cross‐sectional and longitudinal studies (2015‐present) that used magnetic resonance imaging (MRI), diffusion tensor imaging, task‐based functional MRI (fMRI), and/or resting state functional connectivity MRI. These studies were used to identify gray matter, white matter, and brain activity markers of risk and resilience in family history positive (FHP) individuals with a family history of AUD. FHP individuals have greater early adolescent thinning of executive functioning (frontal lobe) regions; however, some studies have reported null effects or greater gray matter volume and thickness relative to family history negative (FHN) peers without familial AUD. FHP individuals also have white matter microstructure alterations, such as reduced integrity of fronto‐striatal pathways. Recent fMRI studies have found greater inhibitory control activity in FHP individuals, while reward‐related findings are mixed. A growing interest in identifying intrinsic connectivity differences between FHP and FHN individuals has emerged in recent years. Familial AUD is related to both structural and functional brain alterations. Research should continue to focus on (1) longitudinal analyses with larger samples, (2) assessment of personal substance use and prenatal exposure to alcohol, (3) the effects of comorbid familial psychopathology, (4) examination of sex‐specific markers of risk and resilience, (5) neural predictors of alcohol use initiation, and (6) brain–behavior relationships. These efforts would aid the design of neurobiologically informed prevention and intervention efforts focused on this at‐risk population.

Magnetic resonance imaging characterization of an α‐synuclein model of Parkinson's disease

AbstractParkinson's disease (PD) is primarily characterized by three histological hallmarks: dopaminergic neuronal degeneration, α‐synuclein accumulation and iron deposition. Over the last years, neuroimaging, particularly magnetic resonance imaging (MRI) has provided invaluable insights into the mechanisms underlying the disease. However, no imaging method has yet been able to translate α‐synuclein protein accumulation and spreading. Amongst the animal models mimicking the disease, the α‐synuclein rat, generated through the injection of human α‐synuclein, has been characterized in terms of behavioural and histological aspects but not thoroughly explored in MRI. The aim of this study is, therefore, to identify the radiological signature from several MRI sequences, while controlling for histological and behavioural characteristics. Rats were assessed for motor and cognitive functions over a 4‐month period. During this time, three MRI sessions, including both morphological and functional sequences, were conducted. Histological studies evaluated the three main hallmarks of PD. The progressive dopaminergic neurodegeneration and the spread of human α‐synuclein corresponded to the level of sensorimotor, attentional and learning deficits observed in this PD model. MRI analyses showed progressive structural abnormalities in the midbrain, diencephalon and several cortical structures, as well as a pattern of hyperconnectivity in the basal ganglia and cortical networks. The regions affected in imaging demonstrated the highest load of human α‐synuclein. This model's structural and functional MRI changes could serve as indirect indicators of α‐synuclein accumulation and its association with impaired non‐motor functions.

Unsupervised definition of two clinical subtypes of Essential tremor and the underlying brain topology

BackgroundEssential tremor (ET) is one of the most prevalent neurological diseases varying considerably in clinical manifestations and prognosis, which indicates the existence of subtypes. Identifying ET subtypes is crucial for explaining clinical heterogeneity. This study aimed to identify ET subtypes using unsupervised clustering analysis based on clinical manifestations and explore underlying brain topology within both functional and structural networks. MethodsWe recruited 103 ET patients and 43 healthy control subjects. K-means clustering analysis was performed to identify ET subtypes based on age of onset, motor and non-motor symptoms. Functional MRI and diffusion tensor imaging data were used to construct functional and structural networks. Global attributes (clustering coefficient, characteristic path length, global efficiency, and local efficiency) and nodal attributes (nodal clustering coefficient, nodal efficiency, and nodal degree centrality) were calculated for topological analysis. ResultsWe identified two subtypes: Subtype 1 (earlier age of onset – without nonmotor symptoms subtype) and Subtype 2 (later age of onset – with nonmotor symptoms subtype). Decreased clustering coefficient and global efficiency, increased characteristic path length were observed in Subtype 2 compared to NC, while only decreased global efficiency was observed in Subtype 1. More widespread brain regions with decreased nodal clustering coefficient were specifically observed in Subtype 2. ConclusionWe identified two ET subtypes based on comprehensive clinical information and revealed that Subtype 2 may be a more malignant subtype. Our study firstly unsupervisedly identifies the clinical heterogeneity of ET and provides neuroimaging evidence for better understanding the underlying disease biology.

Progressive Bulbar Palsy Care: Exploring the Potential of Functional MRI for Early Detection

Progressive bulbar palsy (PBP) is a neurodegenerative disorder impacting motor neurons involved in speech, swallowing, and breathing. Early diagnosis is challenging due to limited diagnostic tools. Functional MRI (fMRI) holds promise for detecting early biomarkers and tracking disease progression, potentially improving clinical management. This study aims to explore the utility of fMRI in developing imaging biomarkers for the early detection, prognostication, and monitoring of PBP. By addressing existing gaps in the literature, the research seeks to demonstrate how fMRI can enhance diagnostic accuracy, provide predictive insights into disease progression, and improve patient outcomes. A systematic literature review was conducted using databases such as Google Scholar. The search focused on articles published from 2018 onwards, using keywords related to PBP and fMRI. Inclusion criteria included peer-reviewed studies involving human subjects relevant to the use of fMRI in PBP. Exclusion criteria excluded non-peer-reviewed articles, non-English publications, and studies not specifically addressing fMRI in PBP. Data synthesis involved both qualitative and quantitative analyses of identified biomarkers, diagnostic accuracy, and prognostic value. The review identified several fMRI biomarkers that show potential for early detection and monitoring of PBP. These biomarkers, derived from functional and structural changes in corticobulbar pathways, offer enhanced sensitivity compared to traditional diagnostic methods. Predictive models based on fMRI metrics can forecast disease progression, aiding in personalized treatment planning. The study concludes that integrating fMRI into clinical practice could lead to earlier interventions, more accurate prognostic assessments, and improved management of PBP, ultimately enhancing patient quality of life and outcomes.

The intrinsic propagation directionality of fMRI infra-slow activity during visual tasks

The temporal order of propagation in the blood-oxygen-level-dependent (BOLD) infra-slow activity (ISA, 0.01–0.1 Hz) of functional magnetic resonance imaging (fMRI) can indicate the functional organization of the brain. While prior studies have revealed the temporal order of propagation of BOLD ISA during rest, how it emerges during cognitive tasks remains unclear. Furthermore, its differences between the gray and white matters at the whole-brain scale are unexplored. In this study, we probed the propagation of BOLD ISA using a publicly available fMRI dataset from participants performing visual detection and discrimination tasks (N = 46, 29 females). We examined the temporal order of propagation based on ISA oscillatory phase differences among brain parcels. During visual task performance, ISA in both the gray and white matters propagated in a direction from the visual cortex to the association cortex, including the default mode network (DMN). This result differs from the previously reported propagation direction during rest that traveled from the visual and somatosensory cortices to the DMN, suggesting that the functional organization may change when performing cognitive tasks. In addition, the propagation in the white matter represented more complex patterns than that in the gray matter, exhibiting that the cingulum preceded DMN. Our results may help the understanding of how task performance alters the sensory-DMN propagation according of ISA.

MEEF criterion-based spline adaptive filtering algorithm and its application

This paper presents an innovative the minimum error entropy with fiducial points (MEEF)-based spline adaptive filtering (S-AF) algorithm, called SAF-MEEF algorithm, which outperforms the conventional SAF algorithms that use the mean square error (MSE) criterion in reducing non-Gaussian interference. To overcome the limitation of the fixed step-size, a variable step-size strategy is also developed, resulting in the SAF-VMEEF algorithm, which improves the convergence speed and steady-state error performance. Furthermore, the computational complexity and convergence analysis of the SAF-MEEF are discussed. Nonlinear system identification simulations test the performance of the presented algorithms. Furthermore, this article accomplishes the application of nonlinear active noise control (ANC). Their effectiveness and robustness against non-Gaussian noise are demonstrated in different experimental scenarios, including α-stable noise, real-world functional magnetic resonance imaging (fMRI) noise, and real-life server room (SR) noise.