Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies. Lee et al. demonstrate that dopaminergic medication-induced changes in spatiotemporal brain state dynamics during a working memory task correlate with individual differences in performance gains in participants with Parkinson’s disease.

Speech impediments are a prominent yet understudied symptom of Parkinson’s disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN’s dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication’s downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.

The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the male rat brain. Optogenetic stimulation of Chronos-expressing AI neurons suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.

Dopaminergic medication is widely used to alleviate motor symptoms of Parkinson's disease, but these medications also impact cognition with significant variability across patients. It is hypothesized that dopaminergic medication impacts cognition and working memory in Parkinson's disease by modulating frontoparietal-basal ganglia cognitive control circuits, but little is known about the underlying causal signalling mechanisms and their relation to individual differences in response to dopaminergic medication. Here we use a novel state-space computational model with ultra-fast (490 ms resolution) functional MRI to investigate dynamic causal signalling in frontoparietal-basal ganglia circuits associated with working memory in 44 Parkinson's disease patients ON and OFF dopaminergic medication, as well as matched 36 healthy controls. Our analysis revealed aberrant causal signalling in frontoparietal-basal ganglia circuits in Parkinson's disease patients OFF medication. Importantly, aberrant signalling was normalized by dopaminergic medication and a novel quantitative distance measure predicted individual differences in cognitive change associated with medication in Parkinson's disease patients. These findings were specific to causal signalling measures, as no such effects were detected with conventional non-causal connectivity measures. Our analysis also identified a specific frontoparietal causal signalling pathway from right middle frontal gyrus to right posterior parietal cortex that is impaired in Parkinson's disease. Unlike in healthy controls, the strength of causal interactions in this pathway did not increase with working memory load and the strength of load-dependent causal weights was not related to individual differences in working memory task performance in Parkinson's disease patients OFF medication. However, dopaminergic medication in Parkinson's disease patients reinstated the relation with working memory performance. Our findings provide new insights into aberrant causal brain circuit dynamics during working memory and identify mechanisms by which dopaminergic medication normalizes cognitive control circuits. Cai et al. report that in patients with Parkinson's disease, aberrant causal signalling in working memory circuits is normalized by dopaminergic medication and is predictive of cognitive response to medication. The findings reveal neurobiological mechanisms by which dopaminergic medication improves cognitive control.

Cognitive impairment in older adults is a rapidly growing public health concern as the elderly population dramatically grows worldwide. While it is generally assumed that cognitive deficits in older adults are associated with reduced brain flexibility, quantitative evidence has been lacking. Here, we investigate brain flexibility in healthy older adults (ages 60-85) using a novel Bayesian switching dynamical system algorithm and ultrafast temporal resolution (TR = 490 ms) whole-brain fMRI data during performance of a Sternberg working memory task. We identify latent brain states and characterize their dynamic temporal properties, including state transitions, associated with encoding, maintenance, and retrieval. Crucially, we demonstrate that brain inflexibility is associated with slower and more fragmented transitions between latent brain states, and that brain inflexibility mediates the relation between age and cognitive inflexibility. Our study provides a novel neurocomputational framework for investigating latent dynamic circuit processes underlying brain flexibility and cognition in the context of aging.

BACKGROUND: Autism spectrum disorder (ASD) is among the most pervasive neurodevelopmental disorders, yet the neurobiology of ASD is still poorly understood because inconsistent findings from underpowered individual studies preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms. METHODS: We leverage multiple brain imaging cohorts and exciting recent advances in explainable artificial intelligence to develop a novel spatiotemporal deep neural network (stDNN) model, which identifies robust and interpretable dynamic brain markers that distinguish ASD from neurotypical control subjects and predict clinical symptom severity. RESULTS: stDNN achieved consistently high classification accuracies in cross-validation analysis of data from the multisite ABIDE (Autism Brain Imaging Data Exchange) cohort (n = 834). Crucially, stDNN also accurately classified data from independent Stanford ( n = 202) and GENDAAR (Gender Exploration of Neurogenetics and Development to Advanced Autism Research) (n = 90) cohorts without additional training. stDNN could not distinguish attentiondeficit/hyperactivity disorder from neurotypical control subjects, highlighting the model's specificity. Explainable artificial intelligence revealed that brain features associated with the posterior cingulate cortex and precuneus, dorsolateral and ventrolateral prefrontal cortex, and superior temporal sulcus, which anchor the default mode network, cognitive control, and human voice processing systems, respectively, most clearly distinguished ASD from neurotypical control subjects in the three cohorts. Furthermore, features associated with the posterior cingulate cortex and precuneus nodes of the default mode network emerged as robust predictors of the severity of core social and communication deficits but not restricted/repetitive behaviors in ASD. CONCLUSIONS: Our findings, replicated across independent cohorts, reveal robust individualized functional brain fingerprints of ASD psychopathology, which could lead to more objective and precise phenotypic characterization and targeted treatments.

Introduction Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders. Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms. Objectives Identify robust and interpretable dynamic brain markers that distinguish children with ASD from typically-developing (TD) children and predict clinical symptom severity. Methods We leverage multiple functional brain imaging cohorts (ABIDE, Stanford; N = 1004) and exciting recent advances in explainable artificial intelligence (xAI), to develop a novel multivariate time series deep neural network model that extracts informative brain dynamics features that accurately distinguish between ASD and TD children, and predict clinical symptom severity. Results Our model achieved consistently high classification accuracies in cross-validation analysis of data from the ABIDE cohort. Crucially, despite the differences in symptom profiles, age, and data acquisition protocols, our model also accurately classified data from an independent Stanford cohort without additional training. xAI analyses revealed that brain features associated with the default mode network, and the human voice/face processing and communication systems, most clearly distinguished ASD from TD children in both cohorts. Furthermore, the posterior cingulate cortex emerged as robust predictor of the severity of social and communication deficits in ASD in both cohorts. Conclusions Our findings, replicated across two independent cohorts, reveal robust and neurobiologically interpretable brain features that detect ASD and predict core phenotypic features of ASD, and have the potential to transform our understanding of the etiology and treatment of the disorder. Disclosure No significant relationships.

Correlational studies of humans suggest that exposure to early life stress has long-term effects on neural circuits involved in vulnerability and resilience to mental health disorders. Stress-related mental health disorders are more prevalent in women than in men. Here, female squirrel monkeys are randomized to intermittently stressful (IS) social separations or a non-separated (NS) control condition conducted from 17 to 27 weeks of age. Nine years later in mid-life adulthood, resting-state functional magnetic resonance imaging was employed to parcellate prefrontal cortex (PFC). Resulting subdivisions were then used to characterize functional connectivity within PFC, and between PFC subdivisions and subcortical regions that are known to be altered by stress. Extensive hyper-connectivity of medial and orbitofrontal PFC with amygdala, hippocampus, and striatum was observed in IS compared to NS monkeys. Functional hyper-connectivity in IS monkeys was associated with previously reported indications of diminished anxiety-like behavior induced by prepubertal stress. Hyper-connectivity of PFC with amygdala and with hippocampus was also associated with increased ventral striatal dopamine D2 and/or D3 receptor (DRD2/3) availability assessed with positron emission tomography (PET) of [11C]raclopride binding in adulthood. Ventral striatal DRD2/3 availability has been linked to cognitive control, which plays a key role in stress coping as an aspect of emotion regulation. These findings provide causal support for enduring neurobiological effects of early life stress and suggest novel targets for new treatments of stress-related mental health disorders.

Background. Neuroimaging studies of spinal cord injury (SCI) have mostly examined the functional organization of the cortex, with only limited focus on the subcortical substrates of the injury. However, thalamus is an important modulator and sensory relay that requires investigation at a subnuclei level to gain insight into the neuroplasticity following SCI. Objective. To use resting-state functional magnetic resonance imaging to examine the functional connectivity (FC) of thalamic subnuclei in complete SCI patients. Methods. A seed-based connectivity analysis was applied for 3 thalamic subnuclei: pulvinar, mediodorsal, and ventrolateral nucleus in each hemisphere. A nonparametric 2-sample t test with permutations was applied for each of the 6 thalamic seeds to compute FC differences between 22 healthy controls and 19 complete SCI patients with paraplegia. Results. Connectivity analysis showed a decrease in the FC of the bilateral mediodorsal nucleus with right superior temporal gyrus and anterior cingulate cortex in the SCI group. Similarly, the left ventrolateral nucleus exhibited decreased FC with left superior temporal gyrus in SCI group. In contrast, left pulvinar nucleus demonstrated an increase in FC with left inferior frontal gyrus and left inferior parietal lobule in SCI group. Our findings also indicate a negative relationship between postinjury durations and thalamic FC to regions of sensorimotor and visual cortices, where longer postinjury durations (12 months) is associated with higher negative connectivity between these regions. Conclusion. This study provides evidence for reorganization in the thalamocortical connections known to be involved in multisensory integration and affective processing, with possible implications in the generation of sensory abnormalities after SCI.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.

The heterogeneous neuronal subgroups of the basal forebrain corticopetal system (BFcs) have been shown to modulate cortical functions through their cholinergic, gamma-aminobutyric acid-ergic, and glutamatergic projections to the entire cortex. Although previous studies suggested that the basalo-cortical projection system influences various cognitive functions, particularly via its cholinergic component, these studies only focused on certain parts of the BFcs or nearby structures, leaving aside a more systematic picture of the functional connectivity of BFcs subcompartments. Moreover, these studies lacked the high-spatial resolution and the probability maps needed to identify specific subcompartments. Recent advances in the ultra-high field 7T functional magnetic resonance imaging (fMRI) provided potentially unprecedented spatial resolution of functional MRI images to study the subdivision of the BFcs. In this study, the BF space containing corticopetal cells was divided into 3 functionally distinct subdivisions based on functional connection to cortical regions derived from fMRI. The overall functional connection of each BFcs subdivision was examined with a test-retest study. Finally, a meta-analysis was used to study the related functional topics of each BF subdivision. Our results demonstrate distinct functional connectivity patterns of these subdivisions along the rostrocaudal axis of the BF. All three compartments have shown consistent segregation and overlap at specific target regions including the hippocampus, insula, thalamus, and the cingulate gyrus, suggesting functional integration and separation in BFcs.

The corpus callosum is the commissural bridge of white-matter bundles important for the human brain functions. Previous studies have analyzed the structural links between cortical gray-matter networks and subregions of corpus callosum. While meaningful white-matter functional networks (WM-FNs) were recently reported, how these networks functionally link with distinct subregions of corpus callosum remained unknown. The current study used resting-state functional magnetic resonance imaging of the Human Connectome Project test-retest data to identify 10 cerebral WM-FNs in 119 healthy subjects and then parcellated the corpus callosum into distinct subregions based on the functional connectivity between each callosal voxel and above networks. Our results demonstrated the reproducible identification of WM-FNs and their links with known gray-matter functional networks across two runs. Furthermore, we identified reliably parcellated subregions of the corpus callosum, which might be involved in primary and higher order functional systems by functionally connecting with WM-FNs. The current study extended our knowledge about the white-matter functional signals to the intrinsic functional organization of human corpus callosum, which could help researchers understand the neural substrates underlying normal interhemispheric functional connectivity as well as dysfunctions in various mental disorders.

Background: The aim of this study was to compare the relationship between core executive functions and frontoparietal network connections at rest between children who had suffered an arterial ischemic stroke and typically developing peers. Methods: Children diagnosed with arterial ischemic stroke more than two years previously and typically developing controls were included. Executive function (EF) measures comprised inhibition (Go-NoGo task), fluency (category fluency task), processing speed (processing speed tasks), divided attention, working memory (letternumber sequencing), conceptual reasoning (matrices) and EF in everyday life (questionnaire). High-resolution T1-weighted magnetic resonance (MR) structural images and resting-state functional MR imaging were acquired. Independent component analysis was used to identify the frontoparietal network. Functional connections were obtained through correlation matrices; associations between cognitive measures and functional connections through Pearson's correlations. Results: Twenty participants after stroke (7 females; mean age 16.0 years) and 22 controls (13 females; mean age 14.8 years) were examined. Patients and controls performed within the normal range in all executive tasks. Patients who had had a stroke performed significantly less well in tests of fluency, processing speed and conceptual reasoning than controls. Resting-state functional connectivity between the left and right inferior parietal lobe was significantly reduced in patients after pediatric stroke. Fluency, processing speed and perceptual reasoning correlated positively with the interhemispheric inferior parietal lobe connection in patients and controls. Conclusion: Decreased interhemispheric connections after stroke in childhood may indicate a disruption of typical interhemispheric interactions relating to executive functions. The present results emphasize the relationship between functional organization of the brain at rest and cognitive processes.

Meta-analysis of neuroimaging results has proven to be a popular and valuable method to study human brain functions. A number of studies have used meta-analysis to parcellate distinct brain regions. A popular way to perform meta-analysis is typically based on the reported activation coordinates from a number of published papers. However, in addition to the coordinates associated with the different brain regions, the text itself contains considerably amount of additional information. This textual information has been largely ignored in meta-analyses where it may be useful for simultaneously parcellating brain regions and studying their characteristics. By leveraging recent advances in document clustering techniques, we introduce an approach to parcellate the brain into meaningful regions primarily based on the text features present in a document from a large number of studies. This new method is called MAPBOT (Meta-Analytic Parcellation Based On Text). Here, we first describe how the method works and then the application case of understanding the sub-divisions of the thalamus. The thalamus was chosen because of the substantial body of research that has been reported studying this functional and structural structure for both healthy and clinical populations. However, MAPBOT is a general-purpose method that is applicable to parcellating any region(s) of the brain. The present study demonstrates the powerful utility of using text information from neuroimaging studies to parcellate brain regions.

Classical trigeminal neuralgia (TN) is a specific type of neuropathic orofacial pain of which the plasticity of brain structure and connectivity have remained largely unknown. A total of 62 TN patients were included and referred to MRI scans. Voxel‐based morphometry was used to analyze the change of gray matter volume. Resting‐state functional imaging was used to analyze the connectivity between brain regions. The results showed gray matter volume reduction in components of the prefrontal cortex, precentral gyrus, cerebellar tonsil, thalamus, hypothalamus, and nucleus accumbens among right TN patient and in the inferior frontal gyrus, precentral gyrus, cerebellum, thalamus, ventral striatum, and putamen among left TN patients. The connections between the right superior frontal gyrus and right middle frontal gyrus were lower in right TN patients. The connection between the left precentral gyrus and the left superior frontal gyrus was lower while the connection between bilateral thalamus was higher in left TN patients. The changes of volume in bilateral thalamus of right TN patients and left ventral striatum of left TN patients, and the connectivity between bilateral thalamus of left TN patients were moderately correlated with pain duration. These findings suggest that brain regions such as the thalamus may not only be involved in processing of pain stimuli but also be important for the development of TN. The left hemisphere may be dominant in processing and modulation of TN pain signal. Chronification of TN induces volume changes in brain regions which are associated with emotional or cognitive modulation of pain. Hum Brain Mapp 39:609–621, 2018 . © 2017 Wiley Periodicals, Inc.

Various studies have indicated that the thalamus is involved in controlling both cortico-cortical information flow and cortical communication with the rest of the brain. Detailed anatomy and functional connectivity patterns of the thalamocortical system are essential to understanding the cortical organization and pathophysiology of a wide range of thalamus-related neurological and neuropsychiatric diseases. The current study used resting-state fMRI to investigate the topography of the human thalamocortical system from a functional perspective. The thalamus-related cortical networks were identified by performing independent component analysis on voxel-based thalamic functional connectivity maps across a large group of subjects. The resulting functional brain networks were very similar to well-established resting-state network maps. Using these brain network components in a spatial regression model with each thalamic voxel’s functional connectivity map, we localized the thalamic subdivisions related to each brain network. For instance, the medial dorsal nucleus was shown to be associated with the default mode, the bilateral executive, the medial visual networks; and the pulvinar nucleus was involved in both the dorsal attention and the visual networks. These results revealed that a single nucleus may have functional connections with multiple cortical regions or even multiple functional networks, and may be potentially related to the function of mediation or modulation of multiple cortical networks. This observed organization of thalamocortical system provided a reference for studying the functions of thalamic sub-regions. The importance of intrinsic connectivity-based mapping of the thalamocortical relationship is discussed, as well as the applicability of the approach for future studies.

Background: Identifying the ischemic penumbra in acute stroke subjects is important for the clinical decision making process. The aim of this study was to use resting-state functional magnetic resonance singal (fMRI) to investigate the change in the amplitude of low-frequency fluctuations (ALFF) of these subjects in three different subsections of acute stroke regions: the infarct core tissue, the penumbra tissue, and the normal brain tissue. Another aim of this study was to test the feasilbility of consistently detecting the penumbra region of the brain through ALFF analysis.Methods: Sixteen subjects with first-ever acute ischemic stroke were scanned within 27 hours of the onset of stroke using magnetic resonance imaging. The core of infarct regions and penumbra regions were determined by diffusion and perfusion-weighted imaging respectively. The ALFF were measured from resting-state blood oxygen level dependent (BOLD) fMRI scans. The averaged relative ALFF value of each regions were correlated with the time after the onset of stroke.Results: Relative ALFF values were significantly different in the infarct core tissue, penumbra tissue and normal brain tissue. The locations of lesions in the ALFF maps did not match perfectly with diffusion and perfusion-weighted imagings; however, these maps provide a contrast that can be used to differentiate between penumbra brain tissue and normal brain tissue. Significant correlations between time after stroke onset and the relative ALFF values were present in the penumbra tissue but not in the infarct core and normal brain tissue.Conclusion: Preliminary results from this study suggest that the ALFF reflects the underlying neurovascular activity and has a great potential to estimate the brain tissue viability after ischemia. Results also show that the ALFF may contribute to acute stroke imaging for thrombolytic or neuroprotective therapies.

Detecting brain tumors is always an important but challenging task for MRI physicists and radiologists. Many automated brain tumor detection methods have been introduced. Due to the complexity and limitations of those procedures, practical applications of those methods in clinical setting are restricted. Based on a large sample size of acquired normal human datasets, this study provided a new approach to detect grey matter abnormalities in individual subjects. This approach consisted of three steps. First, voxel-based morphometry was performed on T1 images from 1000 normal subjects and each of the five tumor subjects. Second, we computed the distribution of the grey matter intensities of each voxel from 1000 normal subjects. Third, we compared the intensity of each grey matter voxel of tumor patient to its corresponding voxel's distribution of normal subjects. Finally, for each tumor subject, grey matter voxels that were out of 99.9% normal distribution were marked. This method uses large data sets to detect abnormalities and has the potential to be utilized in clinical applications.

Impairment in consciousness is common in acute stroke patients and is correlated with the clinical outcome after stroke. The underlying mechanism is not completely understood, with little known about brain activity and connectivity changes in acute stroke patients having impaired consciousness. In this study, we investigated changes in regional brain activity and brain networks of consciousness impaired stroke patients, as well as the amplitude of spontaneous low frequency fluctuation (ALFF) of each time series. Regional homogeneity (ReHo) of each voxel was measured, and resting state network analysis was consequently conducted. Results from this study demonstrate that, compared to normal subjects, the intensities of ALFF and ReHo, as well as the strength of the default mode network (DMN) connectivity, were significantly decreased in the precuneus and posterior cingulate cortex regions among stroke patients with impaired consciousness. Furthermore, the strength of the DMN was highly correlated with differences in the Glasgow Coma Scale (GCS) scores between the onset time and the scanning time. Results from this study suggest that the resting state fMRI is a feasible tool for the evaluation of acute stroke patients with an early impairment of consciousness. The detailed mechanisms, implications of these brain activities and networks exhibiting changes will require further investigation.

The task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit–symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4 min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and task activations within the masks related to task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and task activations (FTAP: r = 0.32; DSST: r = 0.22) within the task positive network and a small yet reliable correlation within the default mode network (DSST: r = − 0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and task activations; but its contribution to task activations can be accounted for by other neurovascular factors such as the ALFF.