Correlates Of fMRI Research Articles

Multi-scale spiking network model of human cerebral cortex.

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

E.3 fMRI correlates of symptom-specific improvement in STN deep brain stimulation

Background: Subthalamic nucleus (STN) deep brain stimulation (DBS) improves the cardinal symptoms of Parkinson’s disease (PD). However, the therapeutic mechanisms are incompletely understood. By leveraging patient-specific brain responses to DBS using functional magnetic resonance imaging (fMRI) acquired during stimulation, we identify and validate symptom-specific networks associated with clinical improvement. Methods: Forty PD patients with STN-DBS were enrolled for fMRI using a 30-sec DBS-ON/OFF cycling paradigm. The four cardinal motor outcomes of PD were chosen a priori and measured using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale, part III (MDS-UPDRSIII): axial instability, tremor, rigidity, bradykinesia. Stimulation-dependent changes in blood oxygen level-dependent (BOLD) signal were correlated with each symptom. Results: The relationship between BOLD response and outcomes revealed significant networks of clinical response (p<0.001). Using BOLD responses from the network hubs, each symptom-specific model was significantly predictive of actual improvement: axial instability (R2=0.38, p=0.000026), bradykinesia (R2=0.29, p=0.00033), rigidity (R2=0.40, p=0.000013), tremor (R2=0.26, p=0.00073). Conclusions: Using patient-specific imaging, we provide evidence of an association between DBS-evoked fMRI response and individual symptom improvement. Brain networks associated with clinical improvement were different depending on the PD symptom examined, suggesting the presence of symptom-specific networks of efficacy which may allow personalization of DBS therapy.

Cortical mapping in glioma surgery: correlation of fMRI and direct electrical stimulation with Human Connectome Project parcellations.

Noninvasive brain mapping with functional MRI (fMRI) and mapping with direct electrical stimulation (DES) are important tools in glioma surgery, but the evidence is inconclusive regarding the sensitivity and specificity of fMRI. The Human Connectome Project (HCP) proposed a new cortical parcellation that has not been thoroughly tested in a clinical setting. The main goal of this study was to evaluate the correlation of fMRI and DES mapping with HCP areas in a clinical setting, and to evaluate the performance of fMRI mapping in motor and language tasks in patients with glioma, using DES as the gold standard. Forty patients with supratentorial gliomas were examined using preoperative fMRI and underwent awake craniotomy with DES. Functional activation maps were visualized on a 3D representation of the cortex, classified according to HCP areas, and compared with surgical mapping. Functional MRI was successful in identifying language and motor HCP areas in most cases, including novel areas such as 55b and the superior longitudinal fasciculus (SLF). Functional MRI had a sensitivity and specificity of 100% and 71%, respectively, for motor function in HCP area 4. Sensitivity and specificity were different according to the area and fMRI protocol; i.e., semantic protocols performed better in Brodmann area (BA) 55b/peri-sylvian language areas with 100% sensitivity and 20% specificity, and word production protocols in BAs 44 and 45 with 70% sensitivity and 80% specificity. Some compensation patterns could be observed, such as motor activation of the postcentral gyrus in precentral gliomas. HCP areas can be detected in clinical scenarios of glioma surgery. These areas appear relatively stable across patients, but compensation patterns seem to differ, allowing occasional resection of activating areas. Newly described areas such as 55b and SLF can act as critical areas in language networks. Surgical planning should account for these parcellations.

Distinct reserve capacity impacts on default-mode network in response to left angular gyrus-navigated repetitive transcranial magnetic stimulation in the prodromal Alzheimer disease

Default-mode network (DMN) may be the earliest affected network and is associated with cognitive decline in Alzheimer’s disease (AD). Repetitive transcranial magnetic stimulation (rTMS) may help to modulate DMN plasticity. Still, stimulation effects substantially vary across studies and individuals. Global left frontal cortex (gLFC) connectivity, a substitute for reserve capacity, may contribute to the heterogeneous physiological effects of neuro-navigated rTMS. This study investigated the effects of left angular gyrus-navigated rTMS on DMN connectivity in different reserve capacity participants. gLFC connectivity, was computed through resting-state fMRI correlations. Thirty-one prodromal AD patients were divided into low connection group (LCG) and high connection group (HCG) by the median of gLFC connectivity. Distinct reserve capacity impacts on DMN in response to rTMS were identified in these two groups. Then, brain-behavior relationships were examined. gLFC connectivity within a certain range is directly proportional to cognitive reserve ability (i.e., LCG), and the effectiveness of functional connectivity beyond this range decreases (i.e, HCG). Moreover, LCG exhibited increased DMN connectivity and significantly positive memory improvements, while HCG showed a contrary connectivity decline and maintained or slightly improved their cognitive function after neuro-navigated rTMS treatment. The prodromal AD patients with the distinct reserve capacity may benefit differently from left angular gyrus-navigated rTMS, which may lead to increasing attention in defining personalized medicine approach of AD.

Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations.

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Acute stress reduces reward-related neural activity: Evidence from the reward positivity

Stress and blunted reward processing are risk factors for Major Depressive Disorder (MDD). The experience of acute stress reduces fMRI correlates of reward-related neural activity; however, few studies have examined how acute stress impacts measures of reward derived from event-related potentials (ERPs). The current study examined the impact of an acute stressor on the Reward Positivity (RewP), an ERP that indexes reward sensitivity, in twenty-six college students. Participants completed a monetary reward task while they placed their left hand in cold water set at 13 degrees Celsius (i.e., acute stress condition) and again while their hand was placed in room temperature water (i.e., control condition). These conditions were separated by one week and performed in a counter-balanced order across participants. The results revealed that the RewP amplitude was blunted in the acute stress condition compared to the control condition. Moreover, there was a trend toward this effect interacting with self-reported depressive symptoms: the RewP was reduced only among individuals who reported low depressive symptoms. The current study suggests that an acute stressor reduces the RewP, and that this effect might be moderated by current depressive symptoms. Future studies might examine the temporal association between reward processing and stress —and how they interact to predict depressive symptoms. LAY SUMMARY: The current study examined the impact of acute stress on the brain’s reward system. The results indicated that acute stress reduced activity within the brain’s reward system, particularly among individuals with low depressive symptoms.

Disturbed temporal dynamics of episodic retrieval activity with preserved spatial activity pattern in amnestic mild cognitive impairment: A simultaneous EEG-fMRI study

Episodic memory (EM) deficit is the core cognitive dysfunction of amnestic mild cognitive impairment (aMCI). However, the episodic retrieval pattern detected by functional MRI (fMRI) appears preserved in aMCI subjects. To address this discrepancy, simultaneous electroencephalography (EEG)-fMRI recording was employed to determine whether temporal dynamics of brain episodic retrieval activity were disturbed in patients with aMCI. Twenty-six aMCI and 29 healthy control (HC) subjects completed a word-list memory retrieval task during simultaneous EEG-fMRI. The retrieval success activation pattern was detected with fMRI analysis, and the familiarity- and recollection-related components of episodic retrieval activity were identified using event-related potential (ERP) analysis. The fMRI-constrained ERP analysis explored the temporal dynamics of brain activity in the retrieval success pattern, and the ERP-informed fMRI analysis detected fMRI correlates of the ERP components related to familiarity and recollection processes. The two groups exhibited similar retrieval success patterns in the bilateral posteromedial parietal cortex, the left inferior parietal lobule (IPL), and the left lateral prefrontal cortex (LPFC). The fMRI-constrained ERP analysis showed that the aMCI group did not exhibit old/new effects in the IPL and LPFC that were observed in the HC group. In addition, the aMCI group showed disturbed fMRI correlate of ERP recollection component that was associated with inferior EM performance. Therefore, in this study, we identified disturbed temporal dynamics in episodic retrieval activity with a preserved spatial activity pattern in aMCI. Taken together, the simultaneous EEG-fMRI technique demonstrated the potential to identify individuals with a high risk of cognitive deterioration.

Visual experience is not necessary for the development of face-selectivity in the lateral fusiform gyrus

The fusiform face area responds selectively to faces and is causally involved in face perception. How does face-selectivity in the fusiform arise in development, and why does it develop so systematically in the same location across individuals? Preferential cortical responses to faces develop early in infancy, yet evidence is conflicting on the central question of whether visual experience with faces is necessary. Here, we revisit this question by scanning congenitally blind individuals with fMRI while they haptically explored 3D-printed faces and other stimuli. We found robust face-selective responses in the lateral fusiform gyrus of individual blind participants during haptic exploration of stimuli, indicating that neither visual experience with faces nor fovea-biased inputs is necessary for face-selectivity to arise in the lateral fusiform gyrus. Our results instead suggest a role for long-range connectivity in specifying the location of face-selectivity in the human brain.

A critical re-evaluation of fMRI signatures of motor sequence learning.

Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.

Improved autoregressive model for correction of noise serial correlation in fast fMRI.

PurposeIn rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T‐statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high‐order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction.MethodsFast fMRI images were acquired at rest (null data) using a multiband simultaneous multi‐slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc) that employs the corrected Akaike information criterion voxel‐wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event‐related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth).ResultsThe measured false positive characteristics agreed well with the theoretical curve when using the ARAICc, and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method.ConclusionThe ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.

Damage to the structural connectome reflected in resting-state fMRI functional connectivity.

The relationship between structural and functional connectivity has been mostly examined in intact brains. Fewer studies have examined how differences in structure as a result of injury alters function. In this study we analyzed the relationship of structure to function across patients with stroke among whom infarcts caused heterogenous structural damage. We estimated relationships between distinct brain regions of interest (ROIs) from functional MRI in two pipelines. In one analysis pipeline, we measured functional connectivity by using correlation and partial correlation between 114 cortical ROIs. We found fMRI-BOLD partial correlation was altered at more edges as a function of the structural connectome (SC) damage, relative to the correlation. In a second analysis pipeline, we limited our analysis to fMRI correlations between pairs of voxels for which we possess SC information. We found that voxel-level functional connectivity showed the effect of structural damage that we could not see when examining correlations between ROIs. Further, the effects of structural damage on functional connectivity are consistent with a model of functional connectivity, diffusion, which expects functional connectivity to result from activity spreading over multiple edge anatomical paths.

NiBetaSeries: task related correlations in fMRI

NiBetaSeries: task related correlations in fMRI

Temporally intermittent communities in brain fMRI correlation networks

It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.

Thalamic Arousal Network Disturbances in Temporal Lobe Epilepsy and Improvement After Surgery

Abstract INTRODUCTION The effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery. METHODS We examined 26 adult TLE patients and 26 matched control participants and used resting-state functional magnetic resonance imaging (fMRI) to measure functional connectivity between the thalamus (entire thalamus and 19 bilateral thalamic nuclei) and both neocortex and brainstem ascending reticular activating system (ARAS) nuclei. Postoperative imaging was completed for 19 patients > 1 yr after surgery and compared to preoperative baseline. RESULTS Before surgery, TLE patients demonstrated abnormal thalamo-occipital functional connectivity, losing the normal negative fMRI correlation between the intralaminar central lateral (CL) nucleus and medial occipital lobe seen in controls (P < .001, paired t-test). Patients also had abnormal connectivity between ARAS and CL, lower ipsilateral intrathalamic connectivity, and smaller ipsilateral thalamic volume compared to controls (P < .05 for each, paired t-tests). Abnormal brainstem-thalamic connectivity was associated with impaired visuospatial attention (? = −0.50, P = .02, Spearman's rho), while lower intrathalamic connectivity and volume were related to higher frequency of consciousness-sparing seizures (P < .02, Spearman's rho). After epilepsy surgery, patients with improved seizures showed partial recovery of thalamo-occipital and brainstem-thalamic connectivity, with values more closely resembling controls (P < .01 for each, ANOVA). CONCLUSION Overall, TLE patients demonstrate impaired connectivity in thalamic arousal networks that may be involved in visuospatial attention, but these disturbances may partially recover after successful epilepsy surgery. Thalamic arousal network dysfunction may contribute to morbidity in TLE.

Thalamic arousal network disturbances in temporal lobe epilepsy and improvement after surgery

ObjectiveThe effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here, we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery.MethodsWe...

Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension.

Communication requires the abilities to generate and interpret utterances and to infer the beliefs, desires, and goals of others ("Theory of Mind"; ToM). These two abilities have been shown to dissociate: individuals with aphasia retain the ability to think about others' mental states; and individuals with autism are impaired in social reasoning, but their basic language processing is often intact. In line with this evidence from brain disorders, functional MRI (fMRI) studies have shown that linguistic and ToM abilities recruit distinct sets of brain regions. And yet, language is a social tool that allows us to share thoughts with one another. Thus, the language and ToM brain networks must share information despite being implemented in distinct neural circuits. Here, we investigated potential interactions between these networks during naturalistic cognition using functional correlations in fMRI. The networks were functionally defined in individual participants, in terms of preference for sentences over nonwords for language, and for belief inference over physical-event processing for ToM, with both a verbal and a nonverbal paradigm. Although, across experiments, interregion correlations within each network were higher than between-network correlations, we also observed above-baseline synchronization of blood oxygenation level-dependent signal fluctuations between the two networks during rest and story comprehension. This synchronization was functionally specific: neither network was synchronized with the executive control network (functionally defined in terms of preference for a harder over easier version of an executive task). Thus, coordination between the language and ToM networks appears to be an inherent and specific characteristic of their functional architecture. NEW & NOTEWORTHY Humans differ from nonhuman primates in their abilities to communicate linguistically and to infer others' mental states. Although linguistic and social abilities appear to be interlinked onto- and phylogenetically, they are dissociated in the adult human brain. Yet successful communication requires language and social reasoning to work in concert. Using functional MRI, we show that language regions are synchronized with social regions during rest and language comprehension, pointing to a possible mechanism for internetwork interaction.

Intra brainstem connectivity is impaired in chronic fatigue syndrome.

In myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), abnormal MRI correlations with symptom severity and autonomic measures have suggested impaired nerve signal conduction within the brainstem. Here we analyse fMRI correlations to directly test connectivity within and from the brainstem. Resting and task functional MRI (fMRI) were acquired for 45 ME/CFS (Fukuda criteria) and 27 healthy controls (HC). We selected limited brainstem reticular activation system (RAS) regions-of-interest (ROIs) based on previous structural MRI findings in a different ME/CFS cohort (bilateral rostral medulla and midbrain cuneiform nucleus), the dorsal Raphe nucleus, and two subcortical ROIs (hippocampus subiculum and thalamus intralaminar nucleus) reported to have rich brainstem connections. When HC and ME/CFS were analysed separately, significant correlations were detected for both groups during both rest and task, with stronger correlations during task than rest. In ME/CFS, connections were absent between medulla and midbrain nuclei, although hippocampal connections with these nuclei were enhanced. When corresponding correlations from HC and ME/CFS were compared, ME/CFS connectivity deficits were detected within the brainstem between the medulla and cuneiform nucleus and between the brainstem and hippocampus and intralaminar thalamus, but only during task. In CFS/ME, weaker connectivity between some RAS nuclei was associated with increased symptom severity. RAS neuron oscillatory signals facilitate coherence in thalamo-cortical oscillations. Brainstem RAS connectivity deficits can explain autonomic changes and diminish cortical oscillatory coherence which can impair attention, memory, cognitive function, sleep quality and muscle tone, all symptoms of ME/CFS.

Serial correlations in single-subject fMRI with sub-second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.

Resting-state fMRI correlations: From link-wise unreliability to whole brain stability

The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze within- and between-subject variability and test-retest reliability of resting-state functional connectivity (FC) in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. We adopt a statistical framework that enables us to identify different sources of variability in FC. We show that the low reliability of single links can be significantly improved by using multiple scans per subject. Moreover, in contrast to earlier studies, we show that spatial heterogeneity in FC reliability is not significant. Finally, we demonstrate that despite the low reliability of individual links, the information carried by the whole-brain FC matrix is robust and can be used as a functional fingerprint to identify individual subjects from the population.